Out-of-Distribution Generalization
238 papers with code • 2 benchmarks • 4 datasets
Benchmarks
These leaderboards are used to track progress in Out-of-Distribution Generalization
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Libraries
Use these libraries to find Out-of-Distribution Generalization models and implementations
shape bias
UrbanCars
ImageNet-W
Object-Centric Stylized COCO
Most implemented papers
Deep Residual Learning for Image Recognition
KaimingHe/resnet-1k-layers
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CVPR 2016
Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
google-research/vision_transformer
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ICLR 2021
While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited.
Learning Transferable Visual Models From Natural Language Supervision
openai/CLIP
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State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories.
mixup: Beyond Empirical Risk Minimization
facebookresearch/mixup-cifar10
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ICLR 2018
We also find that mixup reduces the memorization of corrupt labels, increases the robustness to adversarial examples, and stabilizes the training of generative adversarial networks.
Masked Autoencoders Are Scalable Vision Learners
facebookresearch/mae
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CVPR 2022
Our MAE approach is simple: we mask random patches of the input image and reconstruct the missing pixels.
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
clovaai/CutMix-PyTorch
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ICCV 2019
Regional dropout strategies have been proposed to enhance the performance of convolutional neural network classifiers.
Improved Regularization of Convolutional Neural Networks with Cutout
Convolutional neural networks are capable of learning powerful representational spaces, which are necessary for tackling complex learning tasks.
Designing Network Design Spaces
facebookresearch/pycls
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CVPR 2020
In this work, we present a new network design paradigm.
Invariant Risk Minimization
facebookresearch/InvariantRiskMinimization
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We introduce Invariant Risk Minimization (IRM), a learning paradigm to estimate invariant correlations across multiple training distributions.
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
google-research/augmix
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ICLR 2020
We propose AugMix, a data processing technique that is simple to implement, adds limited computational overhead, and helps models withstand unforeseen corruptions.
