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Domain Generalization

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하위 태스크: 3
평가표: 21
보존된 평가 기록: 570

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

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/enzomuschik/distilfnd

Deep Residual Learning for Image Recognition

https://github.com/epoc88/PFLD_68pts_Pytorch_2020

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/eric-erki/IdenProf

Deep Residual Learning for Image Recognition

https://github.com/erichson/noisy_mixup

mixup: Beyond Empirical Risk Minimization

https://github.com/erlendd/ddan

Domain-Adversarial Training of Neural Networks

https://github.com/erosenfeld/disagree_discrep

Domain-Adjusted Regression or: ERM May Already Learn Features Sufficient for Out-of-Distribution Generalization

https://github.com/eshoyuan/taop2021-3rd-solution

ResNet strikes back: An improved training procedure in timm

https://github.com/etetteh/sota-data-augmentation-and-optimizers

Improved Regularization of Convolutional Neural Networks with Cutout

https://github.com/ewinata/CIFAR-10

Deep Residual Learning for Image Recognition

https://github.com/explainingai-code/VIT-Pytorch

An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

https://github.com/fL0n9/SKFAC-MindSpore

Deep Residual Learning for Image Recognition

https://github.com/fabio-sim/Depth-Anything-ONNX

DINOv2: Learning Robust Visual Features without Supervision

https://github.com/fabioasdias/ubao

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/fabiofumarola/ultrayolo

Deep Residual Learning for Image Recognition

https://github.com/fabxy/accessibilityCV

Deep Residual Learning for Image Recognition

https://github.com/facebook/fb.resnet.torch

Deep Residual Learning for Image Recognition

https://github.com/facebookarchive/fb.resnet.torch

Deep Residual Learning for Image Recognition

https://github.com/facebookresearch/ClassyVision

mixup: Beyond Empirical Risk Minimization

https://github.com/facebookresearch/ClassyVision/tree/master/examples/vit

An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

https://github.com/facebookresearch/augmentation-corruption

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

https://github.com/facebookresearch/detectron

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/facebookresearch/domainbed_measures

In Search of Lost Domain Generalization

https://github.com/facebookresearch/hiera

Deep Residual Learning for Image Recognition

https://github.com/facebookresearch/highrescanopyheight

DINOv2: Learning Robust Visual Features without Supervision

https://github.com/facebookresearch/multimodal

Masked Autoencoders Are Scalable Vision Learners

https://github.com/facebookresearch/ppuda

A ConvNet for the 2020s

https://github.com/facebookresearch/pycls

Deep Residual Learning for Image Recognition

https://github.com/facebookresearch/stochastic_gradient_push

Deep Residual Learning for Image Recognition

https://github.com/facebookresearch/vip-mae

Masked Autoencoders Are Scalable Vision Learners

https://github.com/facebookresearch/xformers

MetaFormer Baselines for Vision

https://github.com/facundoq/transformational_measures_experiments

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/farinamatteo/zero

Learning to Prompt for Vision-Language Models

https://github.com/fastai/fastai

Deep Residual Learning for Image Recognition

https://github.com/fastforwardlabs/causality-for-ml

Invariant Risk Minimization

https://github.com/faustomorales/vit-keras

An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

https://github.com/fchollet/deep-learning-models

Very Deep Convolutional Networks for Large-Scale Image Recognition

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.