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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/anonymouse-9c53tp182bvz/vireo

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Leveraging Depth and Language for Open-Vocabulary Domain-Generalized Semantic Segmentation

https://github.com/anp-scp/fast_bci

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Evaluating Fast Adaptability of Neural Networks for Brain-Computer Interface

https://github.com/ans92/tlac

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TLAC: Two-stage LMM Augmented CLIP for Zero-Shot Classification

https://github.com/anthonysicilia/change-that-matters-acl2022

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The Change that Matters in Discourse Parsing: Estimating the Impact of Domain Shift on Parser Error

https://github.com/anthonysicilia/pacbayes-adaptation-uai2022

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PAC-Bayesian Domain Adaptation Bounds for Multiclass Learners

https://github.com/antoinedemathelin/dbal

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Discrepancy-Based Active Learning for Domain Adaptation

https://github.com/antoinedemathelin/unsupervised-domain-adaptation-for-star-formation-history

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Unsupervised Domain Adaptation for Constraining Star Formation Histories

https://github.com/antoinedemathelin/wann

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Adversarial Weighting for Domain Adaptation in Regression

https://github.com/anum94/adapteval

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AdaptEval: Evaluating Large Language Models on Domain Adaptation for Text Summarization

https://github.com/arakotom/dp_swd

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Differentially Private Sliced Wasserstein Distance

https://github.com/arakotom/mars_domain_adaptation

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Optimal Transport for Conditional Domain Matching and Label Shift

https://github.com/arcadelab/DeepDRR

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SyntheX: Scaling Up Learning-based X-ray Image Analysis Through In Silico Experiments

https://github.com/arcadelab/fastsam3d_slicer

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FastSAM-3DSlicer: A 3D-Slicer Extension for 3D Volumetric Segment Anything Model with Uncertainty Quantification

https://github.com/arimousa/DDAD

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Anomaly Detection with Conditioned Denoising Diffusion Models

https://github.com/aristotelisballas/biodg

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Towards Domain Generalization for ECG and EEG Classification: Algorithms and Benchmarks

https://github.com/aristotelisballas/cyclemix

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CycleMix: Mixing Source Domains for Domain Generalization in Style-Dependent Data

https://github.com/aristotelisballas/gga

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Gradient-Guided Annealing for Domain Generalization

https://github.com/aristotelisballas/m2cl

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Multi-Scale and Multi-Layer Contrastive Learning for Domain Generalization

https://github.com/arnaudjudge/rl4seg

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Domain Adaptation of Echocardiography Segmentation Via Reinforcement Learning

https://github.com/arnav0400/vit-slim

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One-for-All: Generalized LoRA for Parameter-Efficient Fine-tuning

https://github.com/aroitberg/sims4action

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Let's Play for Action: Recognizing Activities of Daily Living by Learning from Life Simulation Video Games

https://github.com/aroncao49/mopa

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MoPA: Multi-Modal Prior Aided Domain Adaptation for 3D Semantic Segmentation

https://github.com/arplaboratory/satellite-thermal-geo-localization

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Long-range UAV Thermal Geo-localization with Satellite Imagery

https://github.com/artemsavkin/secogan

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Content Disentanglement for Semantically Consistent Synthetic-to-Real Domain Adaptation

https://github.com/aryopg/clinical_peft

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Parameter-Efficient Fine-Tuning of LLaMA for the Clinical Domain

https://github.com/asahi417/tner

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T-NER: An All-Round Python Library for Transformer-based Named Entity Recognition

https://github.com/asbjrnmunk/amaes

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AMAES: Augmented Masked Autoencoder Pretraining on Public Brain MRI Data for 3D-Native Segmentation

https://github.com/asbjrnmunk/mdd-unet

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MDD-UNet: Domain Adaptation for Medical Image Segmentation with Theoretical Guarantees, a Proof of Concept

https://github.com/ashafahi/free_adv_train

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Adversarial Training for Free!

https://github.com/askerlee/segtran

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Medical Image Segmentation Using Squeeze-and-Expansion Transformers

https://github.com/astra-vision/densemtl

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DenseMTL: Cross-task Attention Mechanism for Dense Multi-task Learning

https://github.com/astra-vision/poda

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PØDA: Prompt-driven Zero-shot Domain Adaptation

https://github.com/astra-vision/prolip

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Fine-Tuning CLIP's Last Visual Projector: A Few-Shot Cornucopia

https://github.com/astuti/ILA-DA

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Instance Level Affinity-Based Transfer for Unsupervised Domain Adaptation

https://github.com/atschalz/dc_tabeval

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A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular Data

https://github.com/atuannguyen/dirt

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Domain Invariant Representation Learning with Domain Density Transformations

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