Papers with Code 아카이브
Domain Adaptation
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Domain Adaptation is the task of adapting models across domains. This is motivated by the challenge where the test and training datasets fall from different data distributions due to some factor. Domain adaptation aims to build machine learning models that can be generalized into a target domain and dealing with the discrepancy across domain distributions.
Further readings:
<span style="color:grey; opacity: 0.6">( Image credit: Unsupervised Image-to-Image Translation Networks )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/EPFL-VILAB/XDEnsembles
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
https://github.com/EdenMelaku/Transfer-Learning-Pytorch-Implementation
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/EdenMelaku/Transfer-Learning-Pytorch-Implmentation
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/EightRice/atn_GPT-3
https://github.com/EiroaMD/final_project_IH_pneumonia
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/EleutherAI/gpt-neo
https://github.com/EleutherAI/lm-evaluation-harness
https://github.com/EleutherAI/lm_evaluation_harness
https://github.com/Elman295/Cross-Domain-Gradient-Discrepancy-Minimization-for-Unsupervised-Domain-Adaptation
Cross-Domain Gradient Discrepancy Minimization for Unsupervised Domain Adaptation
https://github.com/Elman295/Maximum-Classifier-Discrepancy-for-Unsupervised-Domain-Adaptation
Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
https://github.com/Elman295/Unsupervised-Domain-Adaptation-by-Backpropagation-
https://github.com/Emma0118/domain-generalization
Feature-Critic Networks for Heterogeneous Domain Generalization
https://github.com/EngineeringSapien/NarutoCV
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Epi-NMT/DADG
Discriminative Adversarial Domain Generalization with Meta-learning based Cross-domain Validation
https://github.com/EricAlcaide/MiniFold
https://github.com/EscVM/RSC-Wrapper
https://github.com/EstherBear/implementation-of-pruning-filters
https://github.com/EverLookNeverSee/diag2model
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/EvgenyVendrov/DeepLearningCourse
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/FaceOnLive/Face-Liveness-Detection-SDK-Linux
S-Adapter: Generalizing Vision Transformer for Face Anti-Spoofing with Statistical Tokens
https://github.com/FinnBehrendt/Mahalanobis-Unsupervised-Anomaly-Detection
https://github.com/Flashkong/Source-Free-Object-Detection-by-Learning-to-Overlook-Domain-Style
https://github.com/FlyEgle/MAE-pytorch
https://github.com/FrancescoSaverioZuppichini/ResNet
https://github.com/Fujiki-Nakamura/ADDA.PyTorch
https://github.com/GJTNB/reading-memo
Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identification
https://github.com/GKalliatakis/Keras-VGG16-places365
https://github.com/GLSS-UDA/GLSS-UDA.github.io
https://github.com/GRAAL-Research/domain_adaptation_of_linear_classifiers
https://github.com/GT-RIPL/L2C
Learning to cluster in order to transfer across domains and tasks
https://github.com/Gabriel-Macias/robust_frcnn
A Robust Learning Approach to Domain Adaptive Object Detection
https://github.com/GabrieleMonte/CoMBInE
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Gintasp/xgan
XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings
https://github.com/GiovanniPasq/DA-Faster-RCNN
Domain Adaptive Faster R-CNN for Object Detection in the Wild
https://github.com/Gitikameher/Domain-Adaptive-Single-View-3D-Reconstruction
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.