Domain Generalization
635 papers with code • 18 benchmarks • 25 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
Benchmarks
These leaderboards are used to track progress in Domain Generalization
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Libraries
Use these libraries to find Domain Generalization models and implementationsDatasets
Fashion-MNIST
Office-Home
DomainNet
PACS
ImageNet-C
ImageNet-R
ImageNet-A
ImageNet-Sketch
Stylized ImageNet
WildDash
Most implemented papers
Deep Residual Learning for Image Recognition
tensorflow/models
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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.
Very Deep Convolutional Networks for Large-Scale Image Recognition
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
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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.
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
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
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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.
Aggregated Residual Transformations for Deep Neural Networks
facebookresearch/ResNeXt
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CVPR 2017
Our simple design results in a homogeneous, multi-branch architecture that has only a few hyper-parameters to set.
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.
A ConvNet for the 2020s
facebookresearch/ConvNeXt
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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.
Domain-Adversarial Training of Neural Networks
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
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.
