Domain Generalization

133 papers with code • 11 benchmarks • 16 datasets

The idea of Domain Generalization is to learn from one or multiple training domains, to extract a domain-agnostic model which can be applied to an unseen domain

Source: Diagram Image Retrieval using Sketch-Based Deep Learning and Transfer Learning

Greatest papers with code

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.

Breast Tumour Classification Domain Generalization +8

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

rwightman/pytorch-image-models ICLR 2020

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

Domain Generalization Image Classification

mixup: Beyond Empirical Risk Minimization

rwightman/pytorch-image-models 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.

Domain Generalization Semi-Supervised Image Classification

When Vision Transformers Outperform ResNets without Pretraining or Strong Data Augmentations

google-research/vision_transformer 3 Jun 2021

Vision Transformers (ViTs) and MLPs signal further efforts on replacing hand-wired features or inductive biases with general-purpose neural architectures.

 Ranked #1 on Domain Generalization on ImageNet-R (Top 1 Accuracy metric)

Domain Generalization Fine-Grained Image Classification +1

Improved Regularization of Convolutional Neural Networks with Cutout

PaddlePaddle/PaddleClas 15 Aug 2017

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

Domain Generalization Image Augmentation +1

Posterior Differential Regularization with f-divergence for Improving Model Robustness

namisan/mt-dnn NAACL 2021

Theoretically, we provide a connection of two recent methods, Jacobian Regularization and Virtual Adversarial Training, under this framework.

Domain Generalization

ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness

rgeirhos/texture-vs-shape ICLR 2019

Convolutional Neural Networks (CNNs) are commonly thought to recognise objects by learning increasingly complex representations of object shapes.

Ranked #6 on Domain Generalization on ImageNet-R (using extra training data)

Domain Generalization Image Classification +1