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Domain Adaptation

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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/dbash/visda2022-org

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VisDA 2022 Challenge: Domain Adaptation for Industrial Waste Sorting

https://github.com/dccastro/Morpho-MNIST

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Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation Learning

https://github.com/debcaldarola/fedsam

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Improving Generalization in Federated Learning by Seeking Flat Minima

https://github.com/declare-lab/domadapter

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UDApter -- Efficient Domain Adaptation Using Adapters

https://github.com/declare-lab/hypertts

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HyperTTS: Parameter Efficient Adaptation in Text to Speech using Hypernetworks

https://github.com/declare-lab/kingdom

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KinGDOM: Knowledge-Guided DOMain adaptation for sentiment analysis

https://github.com/deep-scribe/handwriting-recognition

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Motion-Based Handwriting Recognition and Word Reconstruction

https://github.com/deep-spin/chunk-based_knn-mt

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Chunk-based Nearest Neighbor Machine Translation

https://github.com/deep-spin/efficient_knn_mt

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Efficient Machine Translation Domain Adaptation

https://github.com/deeplearnxmu/knn-mt-dr

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Efficient k-Nearest-Neighbor Machine Translation with Dynamic Retrieval

https://github.com/deeplearnxmu/revisedkey-knn-mt

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Bridging the Domain Gaps in Context Representations for k-Nearest Neighbor Neural Machine Translation

https://github.com/deeplearnxmu/semidqg

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Response Enhanced Semi-supervised Dialogue Query Generation

https://github.com/deepopinion/domain-adapted-atsc

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Adapt or Get Left Behind: Domain Adaptation through BERT Language Model Finetuning for Aspect-Target Sentiment Classification

https://github.com/deeppathology/multiscanner_scc

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Multi-Scanner Canine Cutaneous Squamous Cell Carcinoma Histopathology Dataset

https://github.com/deepskies/deepastrouda

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Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection

https://github.com/deepskies/domainadaptivemveforlensmodeling

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Neural Network Prediction of Strong Lensing Systems with Domain Adaptation and Uncertainty Quantification

https://github.com/deepskies/gcnn_da

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SIDDA: SInkhorn Dynamic Domain Adaptation for Image Classification with Equivariant Neural Networks

https://github.com/deepskies/gnn_domainadapt

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Domain Adaptive Graph Neural Networks for Constraining Cosmological Parameters Across Multiple Data Sets

https://github.com/deepskies/sidda

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SIDDA: SInkhorn Dynamic Domain Adaptation for Image Classification with Equivariant Neural Networks

https://github.com/deeptibhegde/attentiveprototypesfuda

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Attentive Prototypes for Source-free Unsupervised Domain Adaptive 3D Object Detection

https://github.com/delair-ai/DISCA

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Interactive Learning for Semantic Segmentation in Earth Observation

https://github.com/dengzeshuai/srtta

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Efficient Test-Time Adaptation for Super-Resolution with Second-Order Degradation and Reconstruction

https://github.com/derekdijin/adamel-supplementary

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Deep Transfer Learning for Multi-source Entity Linkage via Domain Adaptation

https://github.com/derrickxunu/mpda

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Bridging the Domain Gap for Multi-Agent Perception

https://github.com/devavratTomar/sasan

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Self-Attentive Spatial Adaptive Normalization for Cross-Modality Domain Adaptation

https://github.com/devavrattomar/tesla

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TeSLA: Test-Time Self-Learning With Automatic Adversarial Augmentation

https://github.com/devavrattomar/unmixtns

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Un-Mixing Test-Time Normalization Statistics: Combatting Label Temporal Correlation

https://github.com/devinxzhang/mfuser

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Mamba as a Bridge: Where Vision Foundation Models Meet Vision Language Models for Domain-Generalized Semantic Segmentation

https://github.com/deweihu/adaptdiff

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AdaptDiff: Cross-Modality Domain Adaptation via Weak Conditional Semantic Diffusion for Retinal Vessel Segmentation

https://github.com/dg-cavs/dg-coperception

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Towards Full-scene Domain Generalization in Multi-agent Collaborative Bird's Eye View Segmentation for Connected and Autonomous Driving

https://github.com/dhanajitb/petal

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A Probabilistic Framework for Lifelong Test-Time Adaptation

https://github.com/dhw059/densegnn

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DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and molecules

https://github.com/diagnosisda/dxda

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Missing-Class-Robust Domain Adaptation by Unilateral Alignment for Fault Diagnosis

https://github.com/dial-rpi/spectral-adversarial-data-augmentation

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When Neural Networks Fail to Generalize? A Model Sensitivity Perspective

https://github.com/diamondfan/fairseq

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Towards Better Domain Adaptation for Self-supervised Models: A Case Study of Child ASR

https://github.com/dice-group/TeBaQA

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Knowledge Graph Question Answering using Graph-Pattern Isomorphism

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