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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/raoyongming/GFNet

공식 코드

Global Filter Networks for Image Classification

https://github.com/raphaelmerx/tulun

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TULUN: Transparent and Adaptable Low-resource Machine Translation

https://github.com/rasd3/toda

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Semi-Supervised Domain Adaptation Using Target-Oriented Domain Augmentation for 3D Object Detection

https://github.com/ratschlab/icmseg

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Generalizable Single-Source Cross-modality Medical Image Segmentation via Invariant Causal Mechanisms

https://github.com/raunak-agarwal/factual-consistency-eval

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Zero-shot Factual Consistency Evaluation Across Domains

https://github.com/ravishah1/dcin-cqg

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Color-Quality Invariance for Robust Medical Image Segmentation

https://github.com/rdroste/unisal

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Unified Image and Video Saliency Modeling

https://github.com/reactivetype/simrod

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SimROD: A Simple Adaptation Method for Robust Object Detection

https://github.com/reddyav1/rocog-v2

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Synthetic-to-Real Domain Adaptation for Action Recognition: A Dataset and Baseline Performances

https://github.com/reddyav1/unite

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Unsupervised Video Domain Adaptation with Masked Pre-Training and Collaborative Self-Training

https://github.com/refkxh/DUSA

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DUSA: Decoupled Unsupervised Sim2Real Adaptation for Vehicle-to-Everything Collaborative Perception

https://github.com/renshengji/c-cotta

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Controllable Continual Test-Time Adaptation

https://github.com/renshengji/cotta

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Controllable Continual Test-Time Adaptation

https://github.com/renytek13/soft-prompt-generation-with-cgan

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Soft Prompt Generation for Domain Generalization

https://github.com/rese1f/PoseDA

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Global Adaptation meets Local Generalization: Unsupervised Domain Adaptation for 3D Human Pose Estimation

https://github.com/rezaakb/peft-vit

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Parameter Efficient Fine-tuning of Self-supervised ViTs without Catastrophic Forgetting

https://github.com/rezaqorbani/semla

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Semantic Library Adaptation: LoRA Retrieval and Fusion for Open-Vocabulary Semantic Segmentation

https://github.com/rflamary/JDOT

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Joint Distribution Optimal Transportation for Domain Adaptation

https://github.com/rgeirhos/Stylized-ImageNet

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

https://github.com/ricardooyoung/versatileteacher

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Versatile Teacher: A Class-aware Teacher-student Framework for Cross-domain Adaptation

https://github.com/richardobi/medigan

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High-resolution synthesis of high-density breast mammograms: Application to improved fairness in deep learning based mass detection

https://github.com/ricvolpi/adversarial-feature-augmentation

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Adversarial Feature Augmentation for Unsupervised Domain Adaptation

https://github.com/ricvolpi/adversarial_feature_augmentation

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Adversarial Feature Augmentation for Unsupervised Domain Adaptation

https://github.com/rikiyay/style-transfer-for-digital-pathology

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Learning domain-agnostic visual representation for computational pathology using medically-irrelevant style transfer augmentation

https://github.com/rinongal/StyleGAN-nada

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StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generators

https://github.com/rizhaocai/metapattern_fas

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Learning Meta Pattern for Face Anti-Spoofing

https://github.com/rkobler/TSMNet

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SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG

https://github.com/rmf3mc/covid-comp24

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COVID-19 detection from pulmonary CT scans using a novel EfficientNet with attention mechanism

https://github.com/rnjtsh/graphical-object-detector

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Graphical Object Detection in Document Images

https://github.com/robertlakatos/ragvsfn

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Investigating the performance of Retrieval-Augmented Generation and fine-tuning for the development of AI-driven knowledge-based systems

https://github.com/robflynnyh/Self-Train-Before-You-Transcribe

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Self-Train Before You Transcribe

https://github.com/robin-karlsson0/vice

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ViCE: Improving Dense Representation Learning by Superpixelization and Contrasting Cluster Assignment

https://github.com/robingerster7/ossa

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OSSA: Unsupervised One-Shot Style Adaptation

https://github.com/robustml-eurecom/sbnn

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Binary domain generalization for sparsifying binary neural networks

https://github.com/rochelleni/dat

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Distribution-Aware Continual Test-Time Adaptation for Semantic Segmentation

https://github.com/rockwenjj/syreanet

공식 코드

SyreaNet: A Physically Guided Underwater Image Enhancement Framework Integrating Synthetic and Real Images

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