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Awesome-Out-Of-Distribution-Detection
A professionally curated list of papers, tutorials, books, videos, articles and open-source libraries etc for Out-of-distribution detection, robustness, and generalization
https://github.com/huytransformer/Awesome-Out-Of-Distribution-Detection
- [Scholar
- [Scholar
- (2022) Data Distribution Shifts and Monitoring
- (2020) Out-of-Distribution Detection in Deep Neural Networks
- (2023) How to detect Out-of-Distribution data in the wild?
- (2022) Anomaly detection for OOD and novel category detection
- (2022) Reliable Open-World Learning Against Out-of-distribution Data
- (2022) Challenges and Opportunities in Out-of-distribution Detection
- (2022) Exploring the limits of out-of-distribution detection in vision and biomedical applications
- (2021) Understanding the Failure Modes of Out-of-distribution Generalization
- (2020) Uncertainty and Out-of-Distribution Robustness in Deep Learning
- RoboDepth: Robust Out-Of-Distribution Depth Estimation under Corruptions
- OpenOOD v1.5: Benchmarking Generalized OOD Detection
- RoboDepth: Robust Out-of-distribution Depth Estimation Under Corruptions
- OOD NLP: Revisiting Out-of-distribution Robustness in NLP: Benchmark, Analysis, and LLMs Evaluations
- OOD-CV : A Benchmark for Robustness to Out-of-Distribution Shifts of Individual Nuisances in Natural Images
- Photorealistic Unreal Graphics (PUG)
- PyTorch Out-of-Distribution Detection
- FrOoDo: Framework for Out-of-Distribution Detection
- Generalized Out-of-Distribution Detection: A Survey
- A Unified Survey on Anomaly, Novelty, Open-Set, and Out of-Distribution Detection: Solutions and Future Challenges
- A Survey on Out-of-distribution Detection in NLP
- Robust Out-of-Distribution Detection in Deep Classifiers
- Out of Distribution Generalization in Machine Learning
- Detecting Out-Of-Distribution Samples Via Conditional Distribution Entropy With Optimal Transport
- CONJNORM: Tractable Density Estimation for Out-of-distribution Detection
- Learning With Mixture Of Prototypes For Out-Of-Distribution Detection
- How Does Unlabled Data Provably Help Out-Of-Distribution Detection? - wisc/sal) by Du and Fang et al.
- HYPO: Hyperspherical Out-Of-Distribution Generalization - wisc/hypo) by Bai and Ming et al.
- ImageNet-OOD: Deciphering Modern Out-Of-Distribution Detection Algorithms
- Towards Optimal Feature-Shaping Methods For Out-Of-Distribution Detection
- Out-Of-Distribution Detection With Negative Prompts
- DOS: Diverse Outlier Sampling For Out-Of-Distribution Detection
- NECO: Neural Collapse Based Out-Of-Distribution Detection
- Plugin Estimators For Selective Classification With Out-Of-Distribution Detection
- Image Background Servers As Good Proxy For Out-Of-Distribution Data
- Out-Of-Distribution Detection By Leveraging Between-Layer Transformation Smoothness
- Scaling For Training-Time and Posthoc Out-Of-Distribution Detection Enhancement
- Neuron Activation Coverage: Rethinking Out-Of-Distribution Detection and Generalization
- How To Overcome Curse-of-Dimensionality for Out-of-distribution Detection?
- RoboDepth: Robust Out-Of-Distribution Depth Estimation under Corruptions
- GradOrth: A Simple Yet Efficient Out-of-distribution Detection with Orthogonal Projection of Gradients
- Characterizing Out-of-distribution Error via Optimal Transport
- On the Importancce of Feature Separability in Predicting Out-of-distribution Error
- ATTA: Anomaly-aware Test-Time Adaptation for Out-of-distribution Detection in Segmentation
- Diversified Outlier Exposure for Out-of-distribution Detection via Informative Extrapolation - group/DivOE) by Zhu et al.
- Optimal Parameter and Neuron Pruning for Out-of-distribution Detection
- VRA: Variational Rectified Activation for Out-of-distribution Detection
- GAIA: Delving into Gradient-based Attribution Abnormality for Out-of-distribution Detection
- CADet: Fully Self-supervised Anomaly Detection With Contrastive Learning - CADet) by Guille-Escuret et al.
- RoboDepth: Robust Out-of-distribution Depth Estimation Under Corruptions
- Diversify & Conquer: Outcome-directed Curriculum RL via Out-of-distribution Disagreement
- LoCoOp: Few-Shot Out-of-distribution Detection via Prompt Learning
- Category-Extensible Out-of-distribution Detection via Hierarchical Context Descriptions
- Out-of-distribution Detection Learning With Unreliable Out-of-distribution Sources - group/ATOL) by Zheng and Wang et al.
- Dream the Impossible: Outlier Imagination with Diffusion Models
- Learning To Augment Distributions For Out-of-distribution Detection - group/DAL) by Wang et al.
- In- or Out-of-Distribution Detection via Dual Divergence Estimation
- Nearest Neighbor Guidance for Out-of-Distribution Detection
- DIFFGUARD: Semantic Mismatch-Guided Out-of-Distribution Detection using Pre-trained Diffusion Models - lab/DiffGuard) by Gao et al.
- Understanding the Feature Norm for Out-of-Distribution Detection
- SAFE: Sensitivity-Aware Features for Out-of-Distribution Object Detection
- Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentation
- Simple and Effective Out-of-Distribution Detection via Cosine-based Softmax Loss
- Deep Feature Deblurring Diffusion for Detecting Out-of-Distribution Objects - OOD) by Wu et al.
- Revisit PCA-based technique for Out-of-Distribution Detection - GROUP/pca-based-out-of-distribution-detection) by Guan and Liu et al.
- WDiscOOD: Out-of-Distribution Detection via Whitened Linear Discriminant Analysis
- Anomaly Detection under Distribution Shift - lab/ADShift) by Cao et al.
- Out-of-Distribution Detection for Monocular Depth Estimation
- Unified Out-Of-Distribution Detection: A Model-Specific Perspective
- CLIPN for Zero-Shot OOD Detection: Teaching CLIP to Say No - lab/CLIPN) by Wang et al.
- Distribution Shift Inversion for Out-of-Distribution Prediction - rp/Distribution-Shift-Iverson) by Yu et al.
- Uncertainty-Aware Optimal Transport for Semantically Coherent Out-of-Distribution Detection - OOD) by Lu et al.
- GEN: Pushing the Limits of Softmax-Based Out-of-Distribution Detection
- (NAP) Detection of Out-of-Distribution Samples Using Binary Neuron Activation Patterns - group/nap-ood) by Olber et al.
- Decoupling MaxLogit for Out-of-Distribution Detection
- Balanced Energy Regularization Loss for Out-of-Distribution Detection
- Rethinking Out-of-Distribution (OOD) Detection: Masked Image Modeling Is All You Need
- LINe: Out-of-Distribution Detection by Leveraging Important Neurons - Ahn/LINe-Out-of-Distribution-Detection-by-Leveraging-Important-Neurons) by Ahn et al.
- A Critical Analysis of Out-of-distribution Detection for Document Understanding
- A framework for benchmarking Class-out-of-distribution detection and its application to ImageNet
- Energy-based Out-of-Distribution Detection for Graph Neural Networks - GNNSafe) by Wu et al.
- The Tilted Variational Autoencoder: Improving Out-of-Distribution Detection
- Out-of-Distribution Detection based on In-Distribution Data Patterns Memorization with Modern Hopfield Energy
- Out-of-Distribution Detection and Selective Generation for Conditional Language Models
- Turning the Curse of Heterogeneity in Federated Learning into a Blessing for Out-of-Distribution Detection
- Non-Parametric Outlier Synthesis - wisc/npos) by Tao et al.
- Out-of-distribution Detection with Implicit Outlier Transformation
- A Constrained Bayesian Approach to Out-of-Distribution Prediction
- Why Out-of-Distribution Detection Experiments Are Not Reliable - Subtle Experimental Details Muddle the OOD Detector Rankings
- Unsupervised Out-of-Distribution Detection with Diffusion Inpainting
- Generative Causal Representation Learning for Out-of-Distribution Motion Forecasting
- Model Ratatouille: Recycling Diverse Models for Out-of-Distribution Generalization
- Out-of-Distribution Generalization of Federated Learning via Implicit Invariant Relationships
- Feed Two Birds with One Scone: Exploiting Wild Data for Both Out-of-Distribution Generalization and Detection - pLe638) by Bai et al.
- Concept-based Explanations for Out-of-Distribution Detectors
- Hybrid Energy Based Model in the Feature Space for Out-of-Distribution Detection
- Detecting Out-of-distribution Data through In-distribution Class Prior
- Unleashing Mask: Explore the Intrinsic Out-of-Distribution Detection Capability - Mask) by Zhu et al
- In or Out? Fixing ImageNet Out-of-Distribution Detection Evaluation - cb/NINCO) by Bitterwolf et al.
- READ: Aggregating Reconstruction Error into Out-of-Distribution Detection
- Towards In-Distribution Compatible Out-of-Distribution Detection
- Robustness to Spurious Correlations Improves Semantic Out-of-Distribution Detection
- OpenOOD v1.5: Enhanced Benchmark for Out-of-distribution Detection
- Know Your Space: Inlier and Outlier Construction for Calibrating Medical OOD Detectors
- Linking Neural Collapse and L2 Normalization with Improved Out-of-Distribution Detection in Deep Neural Networks
- ViM: Out-Of-Distribution with Virtual-logit Matching - l0p561in) by Wang et al.
- Neural Mean Discrepancy for Efficient Out-of-Distribution Detection
- Deep Hybrid Models for Out-of-Distribution Detection
- Rethinking Reconstruction Autoencoder-Based Out-of-Distribution Detection
- Unknown-Aware Object Detection: Learning What You Don't Know from Videos in the Wild - wisc/stud) by Du et al.
- OpenOOD: Benchmarking Generalized Out-of-Distribution Detection
- Boosting Out-of-distribution Detection with Typical Features
- GraphDE: A Generative Framework for Debiased Learning and Out-of-Distribution Detection on Graphs
- Out-of-Distribution Detection via Conditional Kernel Independence Model
- Your Out-of-Distribution Detection Method is Not Robust! - lab/ATD) by Azizmalayeri et al.
- Out-of-Distribution Detection with An Adaptive Likelihood Ratio on Informative Hierarchical VAE
- GOOD: A Graph Out-of-Distribution Benchmark
- Is Out-of-Distribution Detection Learnable?
- Towards Out-of-Distribution Sequential Event Prediction: A Causal Treatment
- Delving into Out-of-Distribution Detection with Vision-Language Representations - wisc/MCM) by Ming et al.
- Beyond Mahalanobis Distance for Textual OOD Detection
- Density-driven Regularization for Out-of-distribution Detection
- SIREN: Shaping Representations for Detecting Out-of-Distribution Objects - wisc/siren) by Du et al.
- Mitigating Neural Network Overconfidence with Logit Normalization
- Scaling Out-of-Distribution Detection for Real-World Settings - seg) by Hendrycks et al.
- Open-Sampling: Exploring Out-of-Distribution data for Re-balancing Long-tailed datasets
- Model Agnostic Sample Reweighting for Out-of-Distribution Learning - zho14/MAPLE) by Zhou et al.
- Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed Recognition - science/long-tailed-ood-detection) by Wang et al.
- Breaking Down Out-of-Distribution Detection: Many Methods Based on OOD Training Data Estimate a Combination of the Same Core Quantities - cb/Breaking_Down_OOD_Detection) by Bitterwolf et al.
- Predicting Out-of-Distribution Error with the Projection Norm
- POEM: Out-of-Distribution Detection with Posterior Sampling - wisc/poem) by Ming et al.
- (kNN) Out-of-Distribution Detection with Deep Nearest Neighbors - wisc/knn-ood) by Sun et al.
- Training OOD Detectors in their Natural Habitats - Samuels et al.
- A Statistical Framework For Efficient Out-of-distribution Detection in Deep Neural Networks
- Extremely Simple Activation Shaping for Out-of-Distribution Detection
- Revisiting flow generative models for Out-of-distribution detection
- PI3NN: Out-of-distribution-aware Prediction Intervals from Three Neural Networks
- (ATC) Leveraging unlabeled data to predict out-of-distribution performance
- Igeood: An Information Geometry Approach to Out-of-Distribution Detection
- How to Exploit Hyperspherical Embeddings for Out-of-Distribution Detection? - wisc/cider) by Ming et al.
- VOS: Learning What You Don't Know by Virtual Outlier Synthesis - wisc/vos) by Du et al.
- Variational- and Metric-based Deep Latent Space for Out-Of-Distribution Detection - CS-VIL/vmdls) by Dinari and Freifeld
- Out-of-Distribution Identification: Let Detector Tell Which I Am Not Sure
- Out-of-distribution Detection with Boundary Aware Learning
- Out-of-Distribution Detection with Semantic Mismatch under Masking - lab/MOODCat) by Yang et al.
- Data Invariants to Understand Unsupervised Out-of-Distribution Detection - invariants) by Doorenbos et al.
- Embedding contrastive unsupervised features to cluster in- and out-of-distribution noise in corrupted image datasets
- DICE: Leveraging Sparsification for Out-of-Distribution Detection - wisc/dice) by Sun and Li
- On the Impact of Spurious Correlation for Out-of-distribution Detection - wisc/Spurious_OOD) by Ming et al.
- iDECODe: In-Distribution Equivariance for Conformal Out-of-Distribution Detection
- Provable Guarantees for Understanding Out-of-distribution Detection - OOD-Detection) by Morteza and Li
- Learning Modular Structures That Generalize Out-of-Distribution (Student Abstract)
- Exploiting Mixed Unlabeled Data for Detecting Samples of Seen and Unseen Out-of-Distribution Classes
- Out-of-Distribution Detection Using Union of 1-Dimensional Subspaces
- MOOD: Multi-level Out-of-distribution Detection - wisc/MOOD) by Lin et al.
- MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Space - wisc/large_scale_ood) by Huang and Li
- Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection
- STEP: Out-of-Distribution Detection in the Presence of Limited In-Distribution Labeled Data
- Exploring the Limits of Out-of-Distribution Detection
- Learning Causal Semantic Representation for Out-of-Distribution Prediction - semantic-generative-model) by Liu et al.
- Towards optimally abstaining from prediction with OOD test examples
- Locally Most Powerful Bayesian Test for Out-of-Distribution Detection using Deep Generative Models
- RankFeat: Rank-1 Feature Removal for Out-of-distribution Detection
- ReAct: Out-of-distribution Detection With Rectified Activations - wisc/react) by Sun et al.
- (GradNorm) On the Importance of Gradients for Detecting Distributional Shifts in the Wild - wisc/gradnorm_ood) by Huang et al.
- Watermarking for Out-of-distribution Detection
- Can multi-label classification networks know what they don't know? - wisc/multi-label-ood) by Wang et al.
- SSD: A Unified Framework for Self-Supervised Outlier Detection - group/SSD) by Sehwag et al.
- Multiscale Score Matching for Out-of-Distribution Detection
- Sketching Curvature for Efficient Out-of-Distribution Detection for Deep Neural Networks
- Know Your Limits: Uncertainty Estimation with ReLU Classifiers Fails at Reliable OOD Detection - your-limits) by Ulmer and Cinà
- Understanding Failures in Out-of-Distribution Detection with Deep Generative Models
- Density of States Estimation for Out of Distribution Detection
- Semantically Coherent Out-of-Distribution Detection
- CODEs: Chamfer Out-of-Distribution Examples against Overconfidence Issue
- Deep Residual Flow for Out of Distribution Detection - Flow) by Zisselman and Tamar
- Generalized ODIN: Detecting Out-of-Distribution Image Without Learning From Out-of-Distribution Data - ODIN-TF) by Hsu et al.
- CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances
- Energy-based Out-of-distribution Detection
- OOD-MAML: Meta-Learning for Few-Shot Out-of-Distribution Detection and Classification - metalearning-for-fewshot-outofdistribution-detection-and-classification) by Jeong and Kim
- Towards Maximizing the Representation Gap between In-Domain & Out-of-Distribution Examples - representation-gap) by Nandy et al.
- Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder - Regret) by Xiao et al.
- Why Normalizing Flows Fail to Detect Out-of-Distribution Data
- Towards Neural Networks That Provably Know When They Don't Know - certain-uncertainty) by Meinke et al.
- Detecting Out-of-Distribution Examples with Gram Matrices - ood-detection) by Sastry and Oore
- Out-Of-Distribution Detection for Generalized Zero-Shot Action Recognition - od) by Mandal et al.
- Likelihood Ratios for Out-of-Distribution Detection - FduW9ZWAR4) by Ren et al.
- Unsupervised Out-of-Distribution Detection by Maximum Classifier Discrepancy - Out-of-Distribution-Detection-by-Maximum-Classifier-Discrepancy) by Yu and Aizawa
- WAIC, but Why? Generative Ensembles for Robust Anomaly Detection
- (Mahalanobis) A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks
- Out-of-Distribution Detection using Multiple Semantic Label Representations
- Why ReLU Networks Yield High-Confidence Predictions Far Away From the Training Data and How to Mitigate the Problem - andr/relu_networks_overconfident) by Hein et al.
- Do Deep Generative Models Know What They Don't Know? - Topics-in-Machine-Learning-and-Data-Science/Jia.pdf) by Nalisnick et al.
- (OE) Deep Anomaly Detection with Outlier Exposure - exposure) by Hendrycks et al.
- (ODIN) Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks
- Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples
- Out-of-Distribution Detection Using an Ensemble of Self-Supervised Leave-out Classifiers - of-Leave-out-Classifiers) by Vyas et al.
- Learning Confidence for Out-of-Distribution Detection in Neural Networks - mlrg/confidence_estimation) by DeVries and Taylor
- A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks - detection) by Hendrycks and Gimpel
- Distilling Out-of-distribution Robustness from Vision-Language Foundation Models
- Revisiting Out-of-distribution Robustness in NLP: Benchmark, Analysis, and LLMs Evaluations
- Diversify and Disambiguate: Out-of-Distribution Robustness via Disagreement
- Learning Unforeseen Robustness from Out-of-distribution Data Using Equivariant Domain Translator
- Out-of-Domain Robustness via Targeted Augmentations - gao/targeted-augs) by Gao et al.
- The Evolution of Out-of-Distribution Robustness Throughout Fine-Tuning
- Using Mixup as a Regularizer Can Surprisingly Improve Accuracy & Out-of-Distribution Robustness
- Provably Adversarially Robust Detection of Out-of-Distribution Data (Almost) for Free - OOD-Detection) by Meinke et al.
- Improving Out-of-Distribution Robustness via Selective Augmentation
- A Winning Hand: Compressing Deep Networks Can Improve Out-of-Distribution Robustness
- In-N-Out: Pre-Training and Self-Training using Auxiliary Information for Out-of-Distribution Robustness - lambda/in-n-out) by Xie et al.
- Certifiably Adversarially Robust Detection of Out-of-Distribution Data - cb/GOOD) by Bitterwolf et al.
- Unraveling The Key Components Of OOD Generalization Via Diversification
- Maxmimum Likelihood Estimation Is All You Need For Well-Specified Covariate Shift
- Towards Robust Out-Of-Distribution Generalization Bounds via Sharpness
- Spurious Feature Diversification Improves Out-Of-Distribution Generalization
- HYPO: Hyperspherical Out-of-distribution Generalization
- On the Adversarial Robustness of Out-of-distribution Generalization Models - Adv) by Zou and Liu
- Joint Learning of Label and Environment Causal Independence for Graph Out-of-distribution Generalization
- Environment-Aware Dynamic Graph Leaning for Out-of-distribution Generalization
- Secure Out-of-distribution Task Generalization with Energy-based Models
- Understanding and Improving Feature Learning for Out-of-distribution Generalization
- Improving Out-of-distribution Generalization with Indirection Representations
- Topology-aware Robust Optimization for Out-of-Distribution Generalization
- Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization
- Distilling Large Vision-Language Model with Out-of-Distribution Generalizability
- Feed Two Birds with One Scone: Exploiting Wild Data for Both Out-of-Distribution Generalization and Detection - pLe638) by Bai et al.
- On the Connection between Invariant Learning and Adversarial Training for Out-of-Distribution Generalization
- Certifiable Out-of-Distribution Generalization
- Bayesian Cross-Modal Alignment Learning for Few-Shot Out-of-Distribution Generalization
- Out-of-Distribution Generalization by Neural-Symbolic Joint Training
- Out-of-Distribution Generalization With Causal Invariant Transformations
- OoD-Bench: Quantifying and Understanding Two Dimensions of Out-of-Distribution Generalization
- Learning Invariant Graph Representations for Out-of-Distribution Generalization
- Improving Out-of-Distribution Generalization by Adversarial Training with Structured Priors
- Functional Indirection Neural Estimator for Better Out-of-distribution Generalization
- Multi-Instance Causal Representation Learning for Instance Label Prediction and Out-of-Distribution Generalization
- Assaying Out-Of-Distribution Generalization in Transfer Learning - science/assaying-ood) by Wenzel et al.
- Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs
- Diverse Weight Averaging for Out-of-Distribution Generalization
- ZooD: Exploiting Model Zoo for Out-of-Distribution Generalization
- Certifying Out-of-Domain Generalization for Blackbox Functions - generalization) by Weber et al.
- LOG: Active Model Adaptation for Label-Efficient OOD Generalization
- Fishr: Invariant Gradient Variances for Out-of-Distribution Generalization
- Out-of-distribution Generalization in the Presence of Nuisance-Induced Spurious Correlations - lab/nurd-code-public) by Puli et al.
- Uncertainty Modeling for Out-of-Distribution Generalization
- Invariant Causal Representation Learning for Out-of-Distribution Generalization
- Out-of-Distribution Detection Using an Ensemble of Self Supervised Leave-out Classifiers
- Learning to Balance Specificity and Invariance for In and Out of Domain Generalization
- VITA: A Multi-Source Vicinal Transfer Augmentation Method for Out-of-Distribution Generalization
- Deep Stable Learning for Out-of-Distribution Generalization
- Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization
- On the Out-of-distribution Generalization of Probabilistic Image Modelling
- On Calibration and Out-of-Domain Generalization
- Towards a Theoretical Framework of Out-of-Distribution Generalization
- Out-of-Distribution Generalization in Kernel Regression
- Characterizing Generalization under Out-Of-Distribution Shifts in Deep Metric Learning
- Understanding the failure modes of out-of-distribution generalization
- Accuracy on the Line: on the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization
- Out-of-Distribution Generalization via Risk Extrapolation (REx)
- Can Subnetwork Structure Be the Key to Out-of-Distribution Generalization?
- Graph Convolution for Semi-Supervised Classification: Improved Linear Separability and Out-of-Distribution Generalization - Convolution-for-Semi-Supervised-Classification-Improved-Linear-Separability-and-OoD-Gen.) by Baranwal et al.
- Adaptive Calibrator Ensemble: Navigating Test Set Difficulty in Out-of-Distribution Scenarios - Calibrator-Ensemble) by Zou and Deng et al.
- Not All Out-of-distribution Data Are Harmful to Open-Set Active Learning
- AlberDICE: Addressing Out-of-distribution Joint Actions in Offline Multi-Agent RL via Alternating Stationary Distribution Correction Estimation
- Harnessing Out-Of-Distribution Examples via Augmenting Content and Style
- Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in Out-of-Distribution Generalization
- On the Effectiveness of Out-of-Distribution Data in Self-Supervised Long-Tail Learning
- Out-of-distribution Representation Learning for Time Series Classification
- Exploring Chemical Space with Score-based Out-of-distribution Generation
- The Value of Out-of-Distribution Data
- CLIPood: Generalizing CLIP to Out-of-Distributions
- Unsupervised Road Anomaly Detection with Language Anchors
- GenerSpeech: Towards Style Transfer for Generalizable Out-Of-Domain Text-to-Speech
- Learning Substructure Invariance for Out-of-Distribution Molecular Representations
- Evaluating Out-of-Distribution Performance on Document Image Classifiers
- OOD Link Prediction Generalization Capabilities of Message-Passing GNNs in Larger Test Graphs
- Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution
- Improved StyleGAN-v2 based Inversion for Out-of-Distribution Images
- The Out-of-Distribution Problem in Explainability and Search Methods for Feature Importance Explanations
- POODLE: Improving Few-shot Learning via Penalizing Out-of-Distribution Samples
- Task-Agnostic Undesirable Feature Deactivation Using Out-of-Distribution Data - dmlab/TAUFE) by Park et al.
- Removing Undesirable Feature Contributions Using Out-of-Distribution Data
- A Boundary Based Out-of-Distribution Classifier for Generalized Zero-Shot Learning - Boundary-Based-Out-of-Distribution-Classifier-for-Generalized-Zero-Shot-Learning) by Chen et al.
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