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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/ds3lab/certified-generalization

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Certifying Out-of-Domain Generalization for Blackbox Functions

https://github.com/ds3lab/tableparser

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TableParser: Automatic Table Parsing with Weak Supervision from Spreadsheets

https://github.com/dslrproject/dslr

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DSLR: Dynamic to Static LiDAR Scan Reconstruction Using Adversarially Trained Autoencoder

https://github.com/ducksoup/autodial

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AutoDIAL: Automatic DomaIn Alignment Layers

https://github.com/duguodong7/pcb-merging

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Parameter Competition Balancing for Model Merging

https://github.com/duojun-huang/diana-cvpr2023

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Divide and Adapt: Active Domain Adaptation via Customized Learning

https://github.com/durstewitzlab/oodg-in-dsr

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Out-of-Domain Generalization in Dynamical Systems Reconstruction

https://github.com/duyichao/npda-knn-st

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Non-Parametric Domain Adaptation for End-to-End Speech Translation

https://github.com/dvlab-research/Seg-Zero

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Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement

https://github.com/dvlab-research/decouplenet

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DecoupleNet: Decoupled Network for Domain Adaptive Semantic Segmentation

https://github.com/dvlab-research/parametric-contrastive-learning

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Generalized Parametric Contrastive Learning

https://github.com/dwadden/longchecker

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MultiVerS: Improving scientific claim verification with weak supervision and full-document context

https://github.com/dwadden/multivers

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MultiVerS: Improving scientific claim verification with weak supervision and full-document context

https://github.com/dyballa/generalization

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A separability-based approach to quantifying generalization: which layer is best?

https://github.com/dyh127/Adaptive-Exploration-for-Unsupervised-Person-Re-Identification

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Adaptive Exploration for Unsupervised Person Re-Identification

https://github.com/dyu62/deep-domain-generalization

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Deep Spatial Domain Generalization

https://github.com/dyxu-cuhkcse/smg-learning

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Towards Synchronous Memorizability and Generalizability with Site-Modulated Diffusion Replay for Cross-Site Continual Segmentation

https://github.com/dzhaoxd/seco

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Semantic Connectivity-Driven Pseudo-labeling for Cross-domain Segmentation

https://github.com/dzungdoan6/wstta

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Weakly Supervised Test-Time Domain Adaptation for Object Detection

https://github.com/eacevedo1/audiotext-contextda

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Domain Adaptation Method and Modality Gap Impact in Audio-Text Models for Prototypical Sound Classification

https://github.com/eai-lab/transpl

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TransPL: VQ-Code Transition Matrices for Pseudo-Labeling of Time Series Unsupervised Domain Adaptation

https://github.com/eaplatanios/symphony-mt

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Contextual Parameter Generation for Universal Neural Machine Translation

https://github.com/easezyc/deep-transfer-learning

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Deep Subdomain Adaptation Network for Image Classification

https://github.com/easysam/spap

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SPAP: Simultaneous Demand Prediction and Planning for Electric Vehicle Chargers in a New City

https://github.com/ebennequin/meta-domain-shift

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Bridging Few-Shot Learning and Adaptation: New Challenges of Support-Query Shift

https://github.com/echigot/cactif

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Style Transfer with Diffusion Models for Synthetic-to-Real Domain Adaptation

https://github.com/echopluto/fhda

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Few-shot Hybrid Domain Adaptation of Image Generators

https://github.com/ecker-lab/robust-bdd

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Assessing out-of-domain generalization for robust building damage detection

https://github.com/ecreager/eiil

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Environment Inference for Invariant Learning

https://github.com/eddardd/PyDiL

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Lighter, Better, Faster Multi-Source Domain Adaptation with Gaussian Mixture Models and Optimal Transport

https://github.com/eddardd/demo-dadil

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Multi-Source Domain Adaptation through Dataset Dictionary Learning in Wasserstein Space

https://github.com/eddardd/gmm-otda

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Optimal Transport for Domain Adaptation through Gaussian Mixture Models

https://github.com/eddardd/tep-domain-adaptation

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Benchmarking Domain Adaptation for Chemical Processes on the Tennessee Eastman Process

https://github.com/edwarddo69/d3t

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D3T: Distinctive Dual-Domain Teacher Zigzagging Across RGB-Thermal Gap for Domain-Adaptive Object Detection

https://github.com/eheinert/reducing-texture-bias-of-dnns-via-eed

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Reducing Texture Bias of Deep Neural Networks via Edge Enhancing Diffusion

https://github.com/ehsanei/lar

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Label Alignment Regularization for Distribution Shift

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