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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/hehe91/LA

공식 코드

Different Set Domain Adaptation for Brain-Computer Interfaces: A Label Alignment Approach

https://github.com/hehefan/Unsupervised-Person-Re-identification-Clustering-and-Fine-tuning

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Unsupervised Person Re-identification: Clustering and Fine-tuning

https://github.com/hehxcf/cpc

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Confusing Pair Correction Based on Category Prototype for Domain Adaptation under Noisy Environments

https://github.com/helen1c/mc-panda

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MC-PanDA: Mask Confidence for Panoptic Domain Adaptation

https://github.com/helenqu/connect-later

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Connect Later: Improving Fine-tuning for Robustness with Targeted Augmentations

https://github.com/helioszhao/shade

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Style-Hallucinated Dual Consistency Learning for Domain Generalized Semantic Segmentation

https://github.com/helioszhao/shade-visualdg

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Style-Hallucinated Dual Consistency Learning: A Unified Framework for Visual Domain Generalization

https://github.com/hellodfan/grainspace

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GrainSpace: A Large-scale Dataset for Fine-grained and Domain-adaptive Recognition of Cereal Grains

https://github.com/hellowangqian/UDA-norm-VAE

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Data Augmentation with norm-VAE for Unsupervised Domain Adaptation

https://github.com/hellowangqian/cdspp-hda

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Cross-Domain Structure Preserving Projection for Heterogeneous Domain Adaptation

https://github.com/hellowangqian/domain-adaptation-capls

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Unifying Unsupervised Domain Adaptation and Zero-Shot Visual Recognition

https://github.com/helloworld1973/DNA-DA

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Adversarial Domain Adaptation for Cross-user Activity Recognition Using Diffusion-based Noise-centred Learning

https://github.com/hemanthdv/da-hash

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Deep Hashing Network for Unsupervised Domain Adaptation

https://github.com/hendrycks/imagenet-r

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The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization

https://github.com/hendrycks/natural-adv-examples

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Natural Adversarial Examples

https://github.com/hendrycks/robustness

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Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

https://github.com/heng-yuwen/matspectnet

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MatSpectNet: Material Segmentation Network with Domain-Aware and Physically-Constrained Hyperspectral Reconstruction

https://github.com/henrikmarklund/arm

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Adaptive Risk Minimization: Learning to Adapt to Domain Shift

https://github.com/hesam-92-19/transfer_learning_shm

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Zero-Shot Transfer Learning for Structural Health Monitoring using Generative Adversarial Networks and Spectral Mapping

https://github.com/heyjoonkim/universal_domain_adaptation_for_nlp

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Universal Domain Adaptation for Robust Handling of Distributional Shifts in NLP

https://github.com/hf618/cosmic

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COSMIC: Clique-Oriented Semantic Multi-space Integration for Robust CLIP Test-Time Adaptation

https://github.com/hf618/dtbs

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DTBS: Dual-Teacher Bi-directional Self-training for Domain Adaptation in Nighttime Semantic Segmentation

https://github.com/hhtseng/layer-variational-analysis

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Interpretations of Domain Adaptations via Layer Variational Analysis

https://github.com/hik-lab/slimda

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Slimmable Domain Adaptation

https://github.com/hikaru-nara/dask

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Domain-Adaptive Text Classification with Structured Knowledge from Unlabeled Data

https://github.com/hiker-lw/realrain-1k

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Toward Real-world Single Image Deraining: A New Benchmark and Beyond

https://github.com/hikvision-research/domaingeneralization

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Attention Diversification for Domain Generalization

https://github.com/hila-chefer/targetclip

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Image-Based CLIP-Guided Essence Transfer

https://github.com/hilab-git/dag4mia

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CDDSA: Contrastive Domain Disentanglement and Style Augmentation for Generalizable Medical Image Segmentation

https://github.com/hilab-git/dca-net

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Domain Composition and Attention for Unseen-Domain Generalizable Medical Image Segmentation

https://github.com/hilab-git/fpl-plus

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FPL+: Filtered Pseudo Label-based Unsupervised Cross-Modality Adaptation for 3D Medical Image Segmentation

https://github.com/hilab-git/srpl-sfda

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SRPL-SFDA: SAM-Guided Reliable Pseudo-Labels for Source-Free Domain Adaptation in Medical Image Segmentation

https://github.com/hilab-git/ugtst

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An Uncertainty-guided Tiered Self-training Framework for Active Source-free Domain Adaptation in Prostate Segmentation

https://github.com/hilab-git/upl-sfda

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UPL-SFDA: Uncertainty-aware Pseudo Label Guided Source-Free Domain Adaptation for Medical Image Segmentation

https://github.com/hitachi-speech/EEND

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End-to-End Neural Speaker Diarization with Permutation-Free Objectives

https://github.com/hitsz-tmg/icl-state-vector

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In-Context Learning State Vector with Inner and Momentum Optimization

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