Domain Generalization

506 papers with code • 19 benchmarks • 24 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


Use these libraries to find Domain 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.

Very Deep Convolutional Networks for Large-Scale Image Recognition

tensorflow/models 4 Sep 2014

In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting.

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.

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

tensorflow/tpu ICML 2019

Convolutional Neural Networks (ConvNets) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are available.

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.

Aggregated Residual Transformations for Deep Neural Networks

facebookresearch/ResNeXt CVPR 2017

Our simple design results in a homogeneous, multi-branch architecture that has only a few hyper-parameters to set.

A ConvNet for the 2020s

facebookresearch/ConvNeXt CVPR 2022

The "Roaring 20s" of visual recognition began with the introduction of Vision Transformers (ViTs), which quickly superseded ConvNets as the state-of-the-art image classification model.

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.

Domain-Adversarial Training of Neural Networks

PaddlePaddle/PaddleSpeech 28 May 2015

Our approach is directly inspired by the theory on domain adaptation suggesting that, for effective domain transfer to be achieved, predictions must be made based on features that cannot discriminate between the training (source) and test (target) domains.

AutoAugment: Learning Augmentation Policies from Data

tensorflow/models 24 May 2018

In our implementation, we have designed a search space where a policy consists of many sub-policies, one of which is randomly chosen for each image in each mini-batch.