Out-of-Distribution Generalization

136 papers with code • 2 benchmarks • 2 datasets

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Use these libraries to find Out-of-Distribution Generalization models and implementations

Most implemented papers

Deep Residual Learning for Image Recognition

tensorflow/models 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 ICLR 2021

While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited.

mixup: Beyond Empirical Risk Minimization

facebookresearch/mixup-cifar10 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.

Learning Transferable Visual Models From Natural Language Supervision

openai/CLIP 26 Feb 2021

State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories.

Masked Autoencoders Are Scalable Vision Learners

facebookresearch/mae 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 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

uoguelph-mlrg/Cutout 15 Aug 2017

Convolutional neural networks are capable of learning powerful representational spaces, which are necessary for tackling complex learning tasks.

Designing Network Design Spaces

facebookresearch/pycls CVPR 2020

In this work, we present a new network design paradigm.

AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty

google-research/augmix ICLR 2020

We propose AugMix, a data processing technique that is simple to implement, adds limited computational overhead, and helps models withstand unforeseen corruptions.

Invariant Risk Minimization

facebookresearch/InvariantRiskMinimization 5 Jul 2019

We introduce Invariant Risk Minimization (IRM), a learning paradigm to estimate invariant correlations across multiple training distributions.