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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/shyyhs/CourseraParallelCorpusMining

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Coursera Corpus Mining and Multistage Fine-Tuning for Improving Lectures Translation

https://github.com/silvia1993/HyMOS

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Distance-based Hyperspherical Classification for Multi-source Open-Set Domain Adaptation

https://github.com/silvia1993/ROS

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On the Effectiveness of Image Rotation for Open Set Domain Adaptation

https://github.com/simarkareer/unifiedvideoda

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We're Not Using Videos Effectively: An Updated Domain Adaptive Video Segmentation Baseline

https://github.com/sinamalakouti/CDDMSL

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Semi-Supervised Domain Generalization for Object Detection via Language-Guided Feature Alignment

https://github.com/sinashish/mensa_mtda

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MEnsA: Mix-up Ensemble Average for Unsupervised Multi Target Domain Adaptation on 3D Point Clouds

https://github.com/siqi-feng/dnn-behavioral-regularity

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Deep neural networks for choice analysis: Enhancing behavioral regularity with gradient regularization

https://github.com/siyuanliii/masa

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Matching Anything by Segmenting Anything

https://github.com/siyuanyan1/epvt

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EPVT: Environment-aware Prompt Vision Transformer for Domain Generalization in Skin Lesion Recognition

https://github.com/siyuanyan1/pldg

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Prompt-driven Latent Domain Generalization for Medical Image Classification

https://github.com/sjtu-mxtao/semantic-communication-systems

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Deep Learning-Enabled Semantic Communication Systems with Task-Unaware Transmitter and Dynamic Data

https://github.com/sjtuplayer/few-shot-diffusion

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Phasic Content Fusing Diffusion Model with Directional Distribution Consistency for Few-Shot Model Adaption

https://github.com/skjack/diffusionfake

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DiffusionFake: Enhancing Generalization in Deepfake Detection via Guided Stable Diffusion

https://github.com/sky-visionx/cracksegmentation

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Distribution-aware Noisy-label Crack Segmentation

https://github.com/skyorca/awesome-graph-domain-adaptation-papers

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Graph Domain Adaptation: Challenges, Progress and Prospects

https://github.com/skyorca/bottrans

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BotTrans: A Multi-Source Graph Domain Adaptation Approach for Social Bot Detection

https://github.com/skyorca/opengda

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OpenGDA: Graph Domain Adaptation Benchmark for Cross-network Learning

https://github.com/smileix/cpp

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Integration of Old and New Knowledge for Generalized Intent Discovery: A Consistency-driven Prototype-Prompting Framework

https://github.com/smiles724/brot

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Improving Molecular Representation Learning with Metric Learning-enhanced Optimal Transport

https://github.com/smounsav/tta_bot

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Bag of Tricks for Fully Test-Time Adaptation

https://github.com/snap-research/r2l

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SCoDA: Domain Adaptive Shape Completion for Real Scans

https://github.com/socialfoundations/causal-features

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Do causal predictors generalize better to new domains?

https://github.com/sofiabroome/temporal-shape-dataset

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Recur, Attend or Convolve? On Whether Temporal Modeling Matters for Cross-Domain Robustness in Action Recognition

https://github.com/softsys4ai/SCTL

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Scalable Causal Domain Adaptation

https://github.com/sohyun-l/fifo

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FIFO: Learning Fog-invariant Features for Foggy Scene Segmentation

https://github.com/songruiecho/tacit

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TACIT: A Target-Agnostic Feature Disentanglement Framework for Cross-Domain Text Classification

https://github.com/sonyai/ratp

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DEJA VU: Continual Model Generalization For Unseen Domains

https://github.com/sophiaalthammer/alforrankers

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Annotating Data for Fine-Tuning a Neural Ranker? Current Active Learning Strategies are not Better than Random Selection

https://github.com/sophiaalthammer/patent-lim

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Linguistically Informed Masking for Representation Learning in the Patent Domain

https://github.com/spandan-madan/human_visual_diet

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Improving generalization by mimicking the human visual diet

https://github.com/speech-lab-iitm/pada

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PADA: Pruning Assisted Domain Adaptation for Self-Supervised Speech Representations

https://github.com/spiresearch/cda

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Delving into the Continuous Domain Adaptation

https://github.com/spiresearch/utr

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Uncertainty-Induced Transferability Representation for Source-Free Unsupervised Domain Adaptation

https://github.com/splinterli/sattca

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Scale-aware Test-time Click Adaptation for Pulmonary Nodule and Mass Segmentation

https://github.com/sschrod/adbcr

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Adversarial Distribution Balancing for Counterfactual Reasoning

https://github.com/ssgw320/gdamf

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Cost-effective Framework for Gradual Domain Adaptation with Multifidelity

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