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Domain Adaptation

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하위 태스크: 13
평가표: 58
보존된 평가 기록: 391

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:

A Brief Review of Domain Adaptation

<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/kpeng9510/hyprometa

공식 코드

Mitigating Label Noise using Prompt-Based Hyperbolic Meta-Learning in Open-Set Domain Generalization

https://github.com/kpeng9510/relamix

공식 코드

Exploring Few-Shot Adaptation for Activity Recognition on Diverse Domains

https://github.com/ku-cvlab/maskingdepth

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MaskingDepth: Masked Consistency Regularization for Semi-supervised Monocular Depth Estimation

https://github.com/kuangenzhang/edh

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Ensemble diverse hypotheses and knowledge distillation for unsupervised cross-subject adaptation

https://github.com/kundajelab/abstention

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Maximum Likelihood with Bias-Corrected Calibration is Hard-To-Beat at Label Shift Adaptation

https://github.com/kundajelab/labelshiftexperiments

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Maximum Likelihood with Bias-Corrected Calibration is Hard-To-Beat at Label Shift Adaptation

https://github.com/kunkunlin1221/InstructFLIP

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InstructFLIP: Exploring Unified Vision-Language Model for Face Anti-spoofing

https://github.com/kvarada/constructiveness

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Classifying Constructive Comments

https://github.com/kw01sg/crda

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Consistency Regularization for Domain Adaptation

https://github.com/kwang2049/pytorch-bertflow

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TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning

https://github.com/kwang2049/useb

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TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning

https://github.com/kyusik-cho/cmom

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Domain Adaptive Video Semantic Segmentation via Cross-Domain Moving Object Mixing

https://github.com/kzkadc/regression-tta

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Test-time Adaptation for Regression by Subspace Alignment

https://github.com/kzkadc/reid-tta

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Test-time Similarity Modification for Person Re-identification toward Temporal Distribution Shift

https://github.com/kzkadc/uninfo

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Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption

https://github.com/lafite-yu/mttrans-opensource

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MTTrans: Cross-Domain Object Detection with Mean-Teacher Transformer

https://github.com/lalithjets/domain-adaptation-in-mtl

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Task-Aware Asynchronous Multi-Task Model with Class Incremental Contrastive Learning for Surgical Scene Understanding

https://github.com/lamm-mit/llm-finetuning

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Fine-tuning large language models for domain adaptation: Exploration of training strategies, scaling, model merging and synergistic capabilities

https://github.com/lancopku/pkuseg-python

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PKUSEG: A Toolkit for Multi-Domain Chinese Word Segmentation

https://github.com/lanmng/roadda

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Stagewise Unsupervised Domain Adaptation with Adversarial Self-Training for Road Segmentation of Remote Sensing Images

https://github.com/lapisrocks/DiscreteAdversarialDistillation

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Distilling Out-of-Distribution Robustness from Vision-Language Foundation Models

https://github.com/lavine-lmu/m4adapter

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$m^4Adapter$: Multilingual Multi-Domain Adaptation for Machine Translation with a Meta-Adapter

https://github.com/lavine-lmu/rmlnmt

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Improving Both Domain Robustness and Domain Adaptability in Machine Translation

https://github.com/layumi/AdaBoost_Seg

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Adaptive Boosting for Domain Adaptation: Towards Robust Predictions in Scene Segmentation

https://github.com/layumi/Cifar10-Adaboost

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Adaptive Boosting for Domain Adaptation: Towards Robust Predictions in Scene Segmentation

https://github.com/layumi/Seg-Uncertainty

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Unsupervised Scene Adaptation with Memory Regularization in vivo

https://github.com/layumi/person-reid-3d

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Parameter-Efficient Person Re-identification in the 3D Space

https://github.com/lbktrinh/DRU

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Dynamic Retraining-Updating Mean Teacher for Source-Free Object Detection

https://github.com/lccurious/DRDA

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Discriminative Radial Domain Adaptation

https://github.com/lct-rug-2022/edos-2023

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LCT-1 at SemEval-2023 Task 10: Pre-training and Multi-task Learning for Sexism Detection and Classification

https://github.com/ldkong1205/conda

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ConDA: Unsupervised Domain Adaptation for LiDAR Segmentation via Regularized Domain Concatenation

https://github.com/ldkong1205/openess

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OpenESS: Event-based Semantic Scene Understanding with Open Vocabularies

https://github.com/ldkong1205/transvae

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Unsupervised Video Domain Adaptation for Action Recognition: A Disentanglement Perspective

https://github.com/leaplabthu/unitta

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UniTTA: Unified Benchmark and Versatile Framework Towards Realistic Test-Time Adaptation

https://github.com/learningmatter-mit/uq_singlenn

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Single-model uncertainty quantification in neural network potentials does not consistently outperform model ensembles

https://github.com/lee-group-cmu/lf2i

공식 코드

Classification under Nuisance Parameters and Generalized Label Shift in Likelihood-Free Inference

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