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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/eidoslab/lic-domain-adaptation-with-supervised-adapters

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Domain Adaptation for Learned Image Compression with Supervised Adapters

https://github.com/elchiou/DA

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Harnessing Uncertainty in Domain Adaptation for MRI Prostate Lesion Segmentation

https://github.com/elchiou/beyond-deterministic-translation-for-uda

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Beyond Deterministic Translation for Unsupervised Domain Adaptation

https://github.com/elin24/p2rloss

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Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting

https://github.com/emadeldeen24/ADAST

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ADAST: Attentive Cross-domain EEG-based Sleep Staging Framework with Iterative Self-Training

https://github.com/emadeldeen24/adatime

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ADATIME: A Benchmarking Suite for Domain Adaptation on Time Series Data

https://github.com/emadeldeen24/cotmix

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Contrastive Domain Adaptation for Time-Series via Temporal Mixup

https://github.com/embar111/pgpc

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Pseudolabel guided pixels contrast for domain adaptive semantic segmentation

https://github.com/emnlpanon/mt-paper

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Multi-Domain Adaptation in Neural Machine Translation Through Multidimensional Tagging

https://github.com/emonosuke/emoasr

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Non-autoregressive Error Correction for CTC-based ASR with Phone-conditioned Masked LM

https://github.com/emory-irlab/duqgen

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DUQGen: Effective Unsupervised Domain Adaptation of Neural Rankers by Diversifying Synthetic Query Generation

https://github.com/encounter1997/SFA

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Exploring Sequence Feature Alignment for Domain Adaptive Detection Transformers

https://github.com/endoluminalsurgicalvision-imr/pass

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PASS:Test-Time Prompting to Adapt Styles and Semantic Shapes in Medical Image Segmentation

https://github.com/enesdoruk/TransAdapter

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TransAdapter: Vision Transformer for Feature-Centric Unsupervised Domain Adaptation

https://github.com/enesdoruk/damamba

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DA-Mamba: Domain Adaptive Hybrid Mamba-Transformer Based One-Stage Object Detection

https://github.com/enesdoruk/davimnet

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DA-Mamba: Domain Adaptive Hybrid Mamba-Transformer Based One-Stage Object Detection

https://github.com/engrjavediqbal/MLSL

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MLSL: Multi-Level Self-Supervised Learning for Domain Adaptation with Spatially Independent and Semantically Consistent Labeling

https://github.com/engrjavediqbal/wan

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Weakly Supervised Domain Adaptation for Built-up Region Segmentation in Aerial and Satellite Imagery

https://github.com/envision-research/generalizable-bev

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Towards Generalizable Multi-Camera 3D Object Detection via Perspective Debiasing

https://github.com/epfl-dlab/invariant-language-models

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Invariant Language Modeling

https://github.com/epfl-imos/cat

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Calibrated Adaptive Teacher for Domain Adaptive Intelligent Fault Diagnosis

https://github.com/epfl-imos/simple-sfod

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Simplifying Source-Free Domain Adaptation for Object Detection: Effective Self-Training Strategies and Performance Insights

https://github.com/epic-kitchens/epic-kitchens-100-annotations

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Rescaling Egocentric Vision

https://github.com/epic-kitchens/epic-kitchens-100-narrator

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Rescaling Egocentric Vision

https://github.com/epochx/opinatt

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Mining fine-grained opinions on closed captions of YouTube videos with an attention-RNN

https://github.com/ericssonresearch/uda-4-tsc

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Deep Unsupervised Domain Adaptation for Time Series Classification: a Benchmark

https://github.com/erictan7/tgp-t

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Compound Text-Guided Prompt Tuning via Image-Adaptive Cues

https://github.com/erikbrorsson/ecap

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ECAP: Extensive Cut-and-Paste Augmentation for Unsupervised Domain Adaptive Semantic Segmentation

https://github.com/erikvalle/consensus-focus-for-object-detection

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Consensus Focus for Object Detection and minority classes

https://github.com/erosinho13/ladd

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Learning Across Domains and Devices: Style-Driven Source-Free Domain Adaptation in Clustered Federated Learning

https://github.com/esa-philab/learning_from_unlabeled_data_for_domain_adaptation_for_semantic_segmentation

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Learning from Unlabelled Data with Transformers: Domain Adaptation for Semantic Segmentation of High Resolution Aerial Images

https://github.com/esyoon7/litta

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LI-TTA: Language Informed Test-Time Adaptation for Automatic Speech Recognition

https://github.com/ethz-asl/ucsa_neural_rendering

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Unsupervised Continual Semantic Adaptation through Neural Rendering

https://github.com/etrontech/vfmseg

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Visual Foundation Models Boost Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation

https://github.com/etzinis/unsup_speech_enh_adaptation

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RemixIT: Continual self-training of speech enhancement models via bootstrapped remixing

https://github.com/eugenechoi2004/cos429_final

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An In-Depth Analysis of Adversarial Discriminative Domain Adaptation for Digit Classification

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