Out of distribution - Jun 20, 2019 · To train our out-of-distribution detector, video features for unseen action categories are synthesized using generative adversarial networks trained on seen action category features. To the best of our knowledge, we are the first to propose an out-of-distribution detector based GZSL framework for action recognition in videos.

 
Sep 15, 2022 · The unique contribution of this paper is two-fold, justified by extensive experiments. First, we present a realistic problem setting of OOD task for skin lesion. Second, we propose an approach to target the long-tailed and fine-grained aspects of the problem setting simultaneously to increase the OOD performance. . Houses for rent dollar300 near me

Mar 21, 2022 · Most of the existing Out-Of-Distribution (OOD) detection algorithms depend on single input source: the feature, the logit, or the softmax probability. However, the immense diversity of the OOD examples makes such methods fragile. There are OOD samples that are easy to identify in the feature space while hard to distinguish in the logit space and vice versa. Motivated by this observation, we ... Jun 1, 2022 · In part I, we considered the case where we have a clean set of unlabelled data and must determine if a new sample comes from the same set. In part II, we considered the open-set recognition scenario where we also have class labels. This is particularly relevant to the real-world deployment of classifiers, which will inevitably encounter OOD data. Jun 1, 2022 · In part I, we considered the case where we have a clean set of unlabelled data and must determine if a new sample comes from the same set. In part II, we considered the open-set recognition scenario where we also have class labels. This is particularly relevant to the real-world deployment of classifiers, which will inevitably encounter OOD data. Jul 1, 2021 · In general, out-of-distribution data refers to data having a distribution different from that of training data. In the classification problem, out-of-distribution means data with classes that are not included in the training data. In image classification using the deep neural network, the research has been actively conducted to improve the ... May 15, 2022 · 1. We propose an unsupervised method to distinguish in-distribution from out-of-distribution input. The results indicate that the assumptions and methods of outlier and deep anomaly detection are also relevant to the field of out-of-distribution detection. 2. The method works on the basis of an Isolation Forest. Sep 15, 2022 · The unique contribution of this paper is two-fold, justified by extensive experiments. First, we present a realistic problem setting of OOD task for skin lesion. Second, we propose an approach to target the long-tailed and fine-grained aspects of the problem setting simultaneously to increase the OOD performance. Dec 25, 2020 · Out-of-Distribution Detection in Deep Neural Networks Outline:. A bit on OOD. The term “distribution” has slightly different meanings for Language and Vision tasks. Consider a dog... Approaches to Detect OOD instances:. One class of OOD detection techniques is based on thresholding over the ... ODIN: Out-of-Distribution Detector for Neural Networks Jun 6, 2021 · Near out-of-distribution detection (OOD) is a major challenge for deep neural networks. We demonstrate that large-scale pre-trained transformers can significantly improve the state-of-the-art (SOTA) on a range of near OOD tasks across different data modalities. For instance, on CIFAR-100 vs CIFAR-10 OOD detection, we improve the AUROC from 85% (current SOTA) to more than 96% using Vision ... Oct 28, 2022 · Out-of-Distribution (OOD) detection separates ID (In-Distribution) data and OOD data from input data through a model. This problem has attracted increasing attention in the area of machine learning. OOD detection has achieved good intrusion detection, fraud detection, system health monitoring, sensor network event detection, and ecosystem interference detection. The method based on deep ... Aug 24, 2022 · We include results for four types of out-of-distribution samples: (1) dataset shift, where we evaluate the model on two other datasets with differences in the acquisition and population patterns (2) transformation shift where we apply artificial transformations to our ID data, (3) diagnostic shift, where we compare Covid-19 to non-Covid ... Feb 16, 2022 · Graph machine learning has been extensively studied in both academia and industry. Although booming with a vast number of emerging methods and techniques, most of the literature is built on the in-distribution hypothesis, i.e., testing and training graph data are identically distributed. However, this in-distribution hypothesis can hardly be satisfied in many real-world graph scenarios where ... The outputs of an ensemble of networks can be used to estimate the uncertainty of a classifier. At test time, the estimated uncertainty for out-of-distribution samples turns out to be higher than the one for in-distribution samples. 3. level 2. AnvaMiba. Aug 4, 2020 · The goal of Out-of-Distribution (OOD) generalization problem is to train a predictor that generalizes on all environments. Popular approaches in this field use the hypothesis that such a predictor shall be an \\textit{invariant predictor} that captures the mechanism that remains constant across environments. While these approaches have been experimentally successful in various case studies ... Jun 21, 2021 · 1. Discriminators. A discriminator is a model that outputs a prediction based on sample’s features. Discriminators, such as standard feedforward neural networks or ensemble networks, can be ... To clarify the distinction between in-stock distribution, out-of-stock (OOS) distribution, and loss of distribution, it is essential to understand the dynamics of product availability and stock levels. Let’s refer to Exhibit 29.14, which provides an example of a brand’s incidence of purchase and stocks across four time periods. novelty detection (ND), open set recognition (OSR), out-of-distribution (OOD) detection, and outlier detection (OD). These sub-topics can be similar in the sense that they all define a certain in-distribution, with the common goal of detecting out-of-distribution samples under the open-world assumption. However, subtle differences exist among ... Nov 26, 2021 · Unsupervised out-of-distribution (U-OOD) detection has recently attracted much attention due its importance in mission-critical systems and broader applicability over its supervised counterpart. Despite this increase in attention, U-OOD methods suffer from important shortcomings. By performing a large-scale evaluation on different benchmarks and image modalities, we show in this work that most ... Aug 24, 2022 · We include results for four types of out-of-distribution samples: (1) dataset shift, where we evaluate the model on two other datasets with differences in the acquisition and population patterns (2) transformation shift where we apply artificial transformations to our ID data, (3) diagnostic shift, where we compare Covid-19 to non-Covid ... Dec 25, 2020 · Out-of-Distribution Detection in Deep Neural Networks Outline:. A bit on OOD. The term “distribution” has slightly different meanings for Language and Vision tasks. Consider a dog... Approaches to Detect OOD instances:. One class of OOD detection techniques is based on thresholding over the ... Jan 25, 2021 · The term 'out-of-distribution' (OOD) data refers to data that was collected at a different time, and possibly under different conditions or in a different environment, then the data collected to create the model. They may say that this data is from a 'different distribution'. Data that is in-distribution can be called novelty data. Feb 1, 2023 · TL;DR: We propose a novel out-of-distribution detection method motivated by Modern Hopfield Energy, and futhur derive a simplified version that is effective, efficient and hyperparameter-free. Abstract : Out-of-Distribution (OOD) detection is essential for safety-critical applications of deep neural networks. Dec 25, 2020 · Out-of-Distribution Detection in Deep Neural Networks Outline:. A bit on OOD. The term “distribution” has slightly different meanings for Language and Vision tasks. Consider a dog... Approaches to Detect OOD instances:. One class of OOD detection techniques is based on thresholding over the ... out-of-distribution examples, assuming our training set only contains older defendants referred as in-dis-tribution examples. The fractions of data are only for illustrative purposes. See details of in-distribution vs. out-of-distribution setup in §3.2. assistance, human-AI teams should outperform AI alone and human alone (e.g., in accuracy; also To clarify the distinction between in-stock distribution, out-of-stock (OOS) distribution, and loss of distribution, it is essential to understand the dynamics of product availability and stock levels. Let’s refer to Exhibit 29.14, which provides an example of a brand’s incidence of purchase and stocks across four time periods. Jan 22, 2019 · Out-of-distribution detection using an ensemble of self supervised leave-out classifiers A. Vyas, N. Jammalamadaka, X. Zhu, D. Das, B. Kaul, and T. L. Willke, “Out-of-distribution detection using an ensemble of self supervised leave-out classifiers,” in European Conference on Computer Vision, 2018, pp. 560–574. ing data distribution p(x;y). At inference time, given an input x02Xthe goal of OOD detection is to identify whether x0is a sample drawn from p(x;y). 2.2 Types of Distribution Shifts As in (Ren et al.,2019), we assume that any repre-sentation of the input x, ˚(x), can be decomposed into two independent and disjoint components: the background ... Mar 21, 2022 · Most of the existing Out-Of-Distribution (OOD) detection algorithms depend on single input source: the feature, the logit, or the softmax probability. However, the immense diversity of the OOD examples makes such methods fragile. There are OOD samples that are easy to identify in the feature space while hard to distinguish in the logit space and vice versa. Motivated by this observation, we ... marginal distribution of P X,Y for the input variable Xby P 0.Given a test input x ∈X, the problem of out-of-distribution detection can be formulated as a single-sample hypothesis testing task: H 0: x ∼P 0, vs. H 1: x ≁P 0. (1) Here the null hypothesis H 0 implies that the test input x is an in-distribution sample. The goal of CVF Open Access Aug 29, 2023 · ODIN is a preprocessing method for inputs that aims to increase the discriminability of the softmax outputs for In- and Out-of-Distribution data. Implements the Mahalanobis Method. Implements the Energy Score of Energy-based Out-of-distribution Detection. Uses entropy to detect OOD inputs. Implements the MaxLogit method. Jul 1, 2021 · In the classification problem, out-of-distribution data means data with classes not included in the training data. Detecting such out-of-distribution data is a critical problem in the stability of an image classification model using deep learning [10 ]. We define wafer map data with a form other than the 16 types of wafer maps corresponding to ... Sep 15, 2022 · The unique contribution of this paper is two-fold, justified by extensive experiments. First, we present a realistic problem setting of OOD task for skin lesion. Second, we propose an approach to target the long-tailed and fine-grained aspects of the problem setting simultaneously to increase the OOD performance. While out-of-distribution (OOD) generalization, robustness, and detection have been discussed in works related to reducing existential risks from AI (e.g., [Amodei et al., 2016, Hendrycks et al., 2022b]) the truth is that the vast majority of distribution shifts are not directly related to existential risks. Mar 21, 2022 · Most of the existing Out-Of-Distribution (OOD) detection algorithms depend on single input source: the feature, the logit, or the softmax probability. However, the immense diversity of the OOD examples makes such methods fragile. There are OOD samples that are easy to identify in the feature space while hard to distinguish in the logit space and vice versa. Motivated by this observation, we ... The outputs of an ensemble of networks can be used to estimate the uncertainty of a classifier. At test time, the estimated uncertainty for out-of-distribution samples turns out to be higher than the one for in-distribution samples. 3. level 2. AnvaMiba. Mar 25, 2022 · All solutions mentioned above, such as regularization, multimodality, scaling, and invariant risk minimization, can improve distribution shift and out-of-distribution generalization, ultimately ... Aug 24, 2022 · We include results for four types of out-of-distribution samples: (1) dataset shift, where we evaluate the model on two other datasets with differences in the acquisition and population patterns (2) transformation shift where we apply artificial transformations to our ID data, (3) diagnostic shift, where we compare Covid-19 to non-Covid ... Oct 28, 2022 · Out-of-Distribution (OOD) detection separates ID (In-Distribution) data and OOD data from input data through a model. This problem has attracted increasing attention in the area of machine learning. OOD detection has achieved good intrusion detection, fraud detection, system health monitoring, sensor network event detection, and ecosystem interference detection. The method based on deep ... cannot deliver reliable reasoning results when facing out-of-distribution samples. Next, even if supervision signals can be properly propagated between the neural and symbolic models, it is still possible that the NN predicts spurious fea-tures, leading to bad generalization performance (an exam-ple is provided in Sec. 6). Hendrycks & Gimpel proposed a baseline method to detect out-of-distribution examples without further re-training networks. The method is based on an observation that a well-trained neural network tends to assign higher softmax scores to in-distribution examples than out-of-distribution Work done while at Cornell University. 1 Jan 25, 2021 · The term 'out-of-distribution' (OOD) data refers to data that was collected at a different time, and possibly under different conditions or in a different environment, then the data collected to create the model. They may say that this data is from a 'different distribution'. Data that is in-distribution can be called novelty data. Jun 20, 2019 · To train our out-of-distribution detector, video features for unseen action categories are synthesized using generative adversarial networks trained on seen action category features. To the best of our knowledge, we are the first to propose an out-of-distribution detector based GZSL framework for action recognition in videos. Jun 21, 2021 · 1. Discriminators. A discriminator is a model that outputs a prediction based on sample’s features. Discriminators, such as standard feedforward neural networks or ensemble networks, can be ... out-of-distribution examples, assuming our training set only contains older defendants referred as in-dis-tribution examples. The fractions of data are only for illustrative purposes. See details of in-distribution vs. out-of-distribution setup in §3.2. assistance, human-AI teams should outperform AI alone and human alone (e.g., in accuracy; also Apr 21, 2022 · 👋 Hello @recycie, thank you for your interest in YOLOv5 🚀!Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution. trained in the closed-world setting, the out-of-distribution (OOD) issue arises and deteriorates customer experience when the models are deployed in production, facing inputs comingfromtheopenworld[9]. Forinstance,amodelmay wrongly but confidently classify an image of crab into the clappingclass,eventhoughnocrab-relatedconceptsappear in the ... 1ODIN: Out-of-DIstribution detector for Neural networks [21] failures are therefore often silent in that they do not result in explicit errors in the model. The above issue had been formulated as a problem of detecting whether an input data is from in-distribution (i.e. the training distribution) or out-of-distribution (i.e. a distri- Feb 21, 2022 · Most existing datasets with category and viewpoint labels 13,26,27,28 present two major challenges: (1) lack of control over the distribution of categories and viewpoints, or (2) small size. Thus ... Jul 1, 2021 · In the classification problem, out-of-distribution data means data with classes not included in the training data. Detecting such out-of-distribution data is a critical problem in the stability of an image classification model using deep learning [10 ]. We define wafer map data with a form other than the 16 types of wafer maps corresponding to ... Feb 16, 2022 · Graph machine learning has been extensively studied in both academia and industry. Although booming with a vast number of emerging methods and techniques, most of the literature is built on the in-distribution hypothesis, i.e., testing and training graph data are identically distributed. However, this in-distribution hypothesis can hardly be satisfied in many real-world graph scenarios where ... Jul 1, 2021 · In the classification problem, out-of-distribution data means data with classes not included in the training data. Detecting such out-of-distribution data is a critical problem in the stability of an image classification model using deep learning [10 ]. We define wafer map data with a form other than the 16 types of wafer maps corresponding to ... Sep 15, 2022 · The unique contribution of this paper is two-fold, justified by extensive experiments. First, we present a realistic problem setting of OOD task for skin lesion. Second, we propose an approach to target the long-tailed and fine-grained aspects of the problem setting simultaneously to increase the OOD performance. trained in the closed-world setting, the out-of-distribution (OOD) issue arises and deteriorates customer experience when the models are deployed in production, facing inputs comingfromtheopenworld[9]. Forinstance,amodelmay wrongly but confidently classify an image of crab into the clappingclass,eventhoughnocrab-relatedconceptsappear in the ... Feb 16, 2022 · Graph machine learning has been extensively studied in both academia and industry. Although booming with a vast number of emerging methods and techniques, most of the literature is built on the in-distribution hypothesis, i.e., testing and training graph data are identically distributed. However, this in-distribution hypothesis can hardly be satisfied in many real-world graph scenarios where ... Apr 21, 2022 · 👋 Hello @recycie, thank you for your interest in YOLOv5 🚀!Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution. The outputs of an ensemble of networks can be used to estimate the uncertainty of a classifier. At test time, the estimated uncertainty for out-of-distribution samples turns out to be higher than the one for in-distribution samples. 3. level 2. AnvaMiba. out-of-distribution. We present a simple baseline that utilizes probabilities from softmax distributions. Correctly classified examples tend to have greater maxi-mum softmax probabilities than erroneously classified and out-of-distribution ex-amples, allowing for their detection. We assess performance by defining sev- Feb 16, 2022 · To solve this critical problem, out-of-distribution (OOD) generalization on graphs, which goes beyond the I.I.D. hypothesis, has made great progress and attracted ever-increasing attention from the research community. In this paper, we comprehensively survey OOD generalization on graphs and present a detailed review of recent advances in this area. Jul 1, 2021 · In the classification problem, out-of-distribution data means data with classes not included in the training data. Detecting such out-of-distribution data is a critical problem in the stability of an image classification model using deep learning [10 ]. We define wafer map data with a form other than the 16 types of wafer maps corresponding to ... We have summarized the main branches of works for Out-of-Distribution(OOD) Generalization problem, which are classified according to the research focus, including unsupervised representation learning, supervised learning models and optimization methods. For more details, please refer to our survey on OOD generalization. Jun 1, 2022 · In part I, we considered the case where we have a clean set of unlabelled data and must determine if a new sample comes from the same set. In part II, we considered the open-set recognition scenario where we also have class labels. This is particularly relevant to the real-world deployment of classifiers, which will inevitably encounter OOD data. Feb 1, 2023 · TL;DR: We propose a novel out-of-distribution detection method motivated by Modern Hopfield Energy, and futhur derive a simplified version that is effective, efficient and hyperparameter-free. Abstract : Out-of-Distribution (OOD) detection is essential for safety-critical applications of deep neural networks. out-of-distribution. We present a simple baseline that utilizes probabilities from softmax distributions. Correctly classified examples tend to have greater maxi-mum softmax probabilities than erroneously classified and out-of-distribution ex-amples, allowing for their detection. We assess performance by defining sev- Out-of-Distribution (OOD) Detection with Deep Neural Networks based on PyTorch. and is designed such that it should be compatible with frameworks like pytorch-lightning and pytorch-segmentation-models . The library also covers some methods from closely related fields such as Open-Set Recognition, Novelty Detection, Confidence Estimation and ... Sep 15, 2022 · Out-of-Distribution Representation Learning for Time Series Classification. Wang Lu, Jindong Wang, Xinwei Sun, Yiqiang Chen, Xing Xie. Time series classification is an important problem in real world. Due to its non-stationary property that the distribution changes over time, it remains challenging to build models for generalization to unseen ... Sep 15, 2022 · Out-of-Distribution Representation Learning for Time Series Classification. Wang Lu, Jindong Wang, Xinwei Sun, Yiqiang Chen, Xing Xie. Time series classification is an important problem in real world. Due to its non-stationary property that the distribution changes over time, it remains challenging to build models for generalization to unseen ... Nov 26, 2021 · Unsupervised out-of-distribution (U-OOD) detection has recently attracted much attention due its importance in mission-critical systems and broader applicability over its supervised counterpart. Despite this increase in attention, U-OOD methods suffer from important shortcomings. By performing a large-scale evaluation on different benchmarks and image modalities, we show in this work that most ... high-risk applications [5,6]. To solve the problem, out-of-distribution (OOD) detection aims to distinguish and reject test samples with either covariate shifts or semantic shifts or both, so as to prevent models trained on in-distribution (ID) data from producing unreliable predictions [4]. Existing OOD detection methods mostly focus on cal- ODIN: Out-of-Distribution Detector for Neural Networks ing data distribution p(x;y). At inference time, given an input x02Xthe goal of OOD detection is to identify whether x0is a sample drawn from p(x;y). 2.2 Types of Distribution Shifts As in (Ren et al.,2019), we assume that any repre-sentation of the input x, ˚(x), can be decomposed into two independent and disjoint components: the background ... Nov 26, 2021 · Unsupervised out-of-distribution (U-OOD) detection has recently attracted much attention due its importance in mission-critical systems and broader applicability over its supervised counterpart. Despite this increase in attention, U-OOD methods suffer from important shortcomings. By performing a large-scale evaluation on different benchmarks and image modalities, we show in this work that most ... Jun 21, 2021 · 1. Discriminators. A discriminator is a model that outputs a prediction based on sample’s features. Discriminators, such as standard feedforward neural networks or ensemble networks, can be ... Mar 2, 2020 · Out-of-Distribution Generalization via Risk Extrapolation (REx) Distributional shift is one of the major obstacles when transferring machine learning prediction systems from the lab to the real world. To tackle this problem, we assume that variation across training domains is representative of the variation we might encounter at test time, but ... Feb 21, 2022 · Most existing datasets with category and viewpoint labels 13,26,27,28 present two major challenges: (1) lack of control over the distribution of categories and viewpoints, or (2) small size. Thus ... novelty detection (ND), open set recognition (OSR), out-of-distribution (OOD) detection, and outlier detection (OD). These sub-topics can be similar in the sense that they all define a certain in-distribution, with the common goal of detecting out-of-distribution samples under the open-world assumption. However, subtle differences exist among ... Feb 16, 2022 · To solve this critical problem, out-of-distribution (OOD) generalization on graphs, which goes beyond the I.I.D. hypothesis, has made great progress and attracted ever-increasing attention from the research community. In this paper, we comprehensively survey OOD generalization on graphs and present a detailed review of recent advances in this area.

Jun 6, 2021 · Near out-of-distribution detection (OOD) is a major challenge for deep neural networks. We demonstrate that large-scale pre-trained transformers can significantly improve the state-of-the-art (SOTA) on a range of near OOD tasks across different data modalities. For instance, on CIFAR-100 vs CIFAR-10 OOD detection, we improve the AUROC from 85% (current SOTA) to more than 96% using Vision ... . Puppies for sale in charlotte nc under dollar500

out of distribution

Aug 4, 2020 · The goal of Out-of-Distribution (OOD) generalization problem is to train a predictor that generalizes on all environments. Popular approaches in this field use the hypothesis that such a predictor shall be an \\textit{invariant predictor} that captures the mechanism that remains constant across environments. While these approaches have been experimentally successful in various case studies ... Dec 25, 2020 · Out-of-Distribution Detection in Deep Neural Networks Outline:. A bit on OOD. The term “distribution” has slightly different meanings for Language and Vision tasks. Consider a dog... Approaches to Detect OOD instances:. One class of OOD detection techniques is based on thresholding over the ... cause of model crash under distribution shifts, they propose to realize out-of-distribution generalization by decorrelat-ing the relevant and irrelevant features. Since there is no extra supervision for separating relevant features from ir-relevant features, a conservative solution is to decorrelate all features. Jul 1, 2021 · In the classification problem, out-of-distribution data means data with classes not included in the training data. Detecting such out-of-distribution data is a critical problem in the stability of an image classification model using deep learning [10 ]. We define wafer map data with a form other than the 16 types of wafer maps corresponding to ... Feb 21, 2022 · It is well known that fine-tuning leads to better accuracy in-distribution (ID). However, in this paper, we find that fine-tuning can achieve worse accuracy than linear probing out-of-distribution (OOD) when the pretrained features are good and the distribution shift is large. On 10 distribution shift datasets (Breeds-Living17, Breeds-Entity30 ... Aug 31, 2021 · This paper represents the first comprehensive, systematic review of OOD generalization, encompassing a spectrum of aspects from problem definition, methodological development, and evaluation procedures, to the implications and future directions of the field. out-of-distribution. We present a simple baseline that utilizes probabilities from softmax distributions. Correctly classified examples tend to have greater maxi-mum softmax probabilities than erroneously classified and out-of-distribution ex-amples, allowing for their detection. We assess performance by defining sev- Feb 21, 2022 · It is well known that fine-tuning leads to better accuracy in-distribution (ID). However, in this paper, we find that fine-tuning can achieve worse accuracy than linear probing out-of-distribution (OOD) when the pretrained features are good and the distribution shift is large. On 10 distribution shift datasets (Breeds-Living17, Breeds-Entity30 ... 1ODIN: Out-of-DIstribution detector for Neural networks [21] failures are therefore often silent in that they do not result in explicit errors in the model. The above issue had been formulated as a problem of detecting whether an input data is from in-distribution (i.e. the training distribution) or out-of-distribution (i.e. a distri- Mar 3, 2021 · Then, we focus on a certain class of out of distribution problems, their assumptions, and introduce simple algorithms that follow from these assumptions that are able to provide more reliable generalization. A central topic in the thesis is the strong link between discovering the causal structure of the data, finding features that are reliable ... Aug 4, 2020 · The goal of Out-of-Distribution (OOD) generalization problem is to train a predictor that generalizes on all environments. Popular approaches in this field use the hypothesis that such a predictor shall be an \\textit{invariant predictor} that captures the mechanism that remains constant across environments. While these approaches have been experimentally successful in various case studies ... Hendrycks & Gimpel proposed a baseline method to detect out-of-distribution examples without further re-training networks. The method is based on an observation that a well-trained neural network tends to assign higher softmax scores to in-distribution examples than out-of-distribution Work done while at Cornell University. 1 Aug 4, 2020 · The goal of Out-of-Distribution (OOD) generalization problem is to train a predictor that generalizes on all environments. Popular approaches in this field use the hypothesis that such a predictor shall be an \\textit{invariant predictor} that captures the mechanism that remains constant across environments. While these approaches have been experimentally successful in various case studies ... We have summarized the main branches of works for Out-of-Distribution(OOD) Generalization problem, which are classified according to the research focus, including unsupervised representation learning, supervised learning models and optimization methods. For more details, please refer to our survey on OOD generalization. .

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