Alternatives and similar repositories for Episodic-DG

Users that are interested in Episodic-DG are comparing it to the libraries listed below

• deep-real / M-ADA
  The Pytorch implementation for "Learning to Learn Single Domain Generalization" (CVPR 2020)
  ☆148Updated 4 years ago
• roysubhankar / dwt-domain-adaptation
  Code for paper "Unsupervised Domain Adaptation using Feature-Whitening and Consensus Loss" (CVPR 2019)
  ☆64Updated 4 years ago
• zhiheLu / STAR_Stochastic_Classifiers_for_UDA
  ☆22Updated 4 years ago
• prithv1 / DMG
  Code for our ECCV paper -- "Learning to Balance Specificity and Invariance for In and Out of Domain Generalization"
  ☆56Updated 4 years ago
• thuml / TransNorm
  Code release for "Transferable Normalization: Towards Improving Transferability of Deep Neural Networks" (NeurIPS 2019)
  ☆79Updated 4 years ago
• Gorilla-Lab-SCUT / SRDC-CVPR2020
  Code release for Unsupervised Domain Adaptation via Structurally Regularized Deep Clustering (CVPR2020-Oral).
  ☆81Updated 3 years ago
• DeLightCMU / RSC
  This is the official implementation of Self-Challenging Improves Cross-Domain Generalization, ECCV2020
  ☆164Updated 4 years ago
• ygjwd12345 / TransDA
  Official pytorch implement for “Transformer-Based Source-Free Domain Adaptation”
  ☆68Updated 2 years ago
• BIT-DA / TSA
  [CVPR 2021 Oral] Code release for "Transferable Semantic Augmentation for Domain Adaptation"
  ☆74Updated 4 years ago
• cuishuhao / GVB
  Code of Gradually Vanishing Bridge for Adversarial Domain Adaptation (CVPR2020)
  ☆80Updated last year
• RoyalVane / ASM
  ( NeurIPS 2020 ) Adversarial Style Mining for One-Shot Unsupervised Domain Adaptation
  ☆71Updated 3 years ago
• zhmiao / OpenCompoundDomainAdaptation-OCDA
  Pytorch implementation for "Open Compound Domain Adaptation" (CVPR 2020 ORAL)
  ☆139Updated 3 years ago
• mancinimassimiliano / pytorch_wbn
  PyTorch implementation of Weighted Batch-Normalization layers
  ☆37Updated 4 years ago
• thuml / Batch-Spectral-Penalization
  Code release for Transferability vs. Discriminability: Batch Spectral Penalization for Adversarial Domain Adaptation (ICML 2019)
  ☆93Updated 5 years ago
• YU1ut / openset-DA
  Code for 'Open Set Domain Adaptation by Backpropagation'
  ☆75Updated 6 years ago
• YuqiCui / MMD_AAE
  Code refactoring for paper "Domain Generalization with Adversarial Feature Learning" in VLCS datasets.
  ☆56Updated 5 years ago
• sshan-zhao / DG_via_ER
  Domain Generalization via Entropy Regularization, NeurIPS'20
  ☆66Updated 4 years ago
• woozch / DSBN
  Official Implementation of "Domain Specific Batch Normalization for Unsupervised Domain Adaptation (CVPR2019)"
  ☆154Updated last year
• ChrisAllenMing / LtC-MSDA
  Implementation of Learning to Combine: Knowledge Aggregation for Multi-Source Domain Adaptation (ECCV 2020).
  ☆72Updated 3 years ago
• lileicv / PDEN
  ☆37Updated 3 years ago
• hyseob / SagNet
  Reducing Domain Gap by Reducing Style Bias (SagNets)
  ☆102Updated 3 years ago
• xiangjjj / implicit_alignment
  Code for ICML2020 "Implicit Class-Conditioned Domain Alignment for Unsupervised Domain Adaptation"
  ☆90Updated 4 years ago
• emma-sjwang / EISNet
  [ECCV 2020] Learning from Extrinsic and Intrinsic Supervisions for Domain Generalization
  ☆48Updated 2 years ago
• virajprabhu / SENTRY
  PyTorch code for SENTRY: Selective Entropy Optimization via Committee Consistency for Unsupervised DA (ICCV 2021)
  ☆57Updated 3 years ago
• mil-tokyo / dg_mmld
  Pytorch Implementation of Domain Generalization Using a Mixture of Multiple Latent Domains
  ☆102Updated 3 years ago
• tim-learn / BA3US
  code for our ECCV 2020 paper "A Balanced and Uncertainty-aware Approach for Partial Domain Adaptation"
  ☆40Updated 2 years ago
• silvia1993 / ROS
  Rotation-based Open Set
  ☆41Updated 2 years ago
• ksaito-ut / OPDA_BP
  ☆57Updated 6 years ago
• zhangweichen2006 / iCAN
  Collaborative and Adversarial Network for Unsupervised Domain Adaptation(iCAN)
  ☆34Updated 4 years ago
• EasonApolo / mstn
  PyTorch reimplementation of Moving Semantic Transfer Network
  ☆36Updated 6 years ago