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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/visinf/self-adaptive

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Semantic Self-adaptation: Enhancing Generalization with a Single Sample

https://github.com/vision4robotics/DaDiff

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DaDiff: Domain-aware Diffusion Model for Nighttime UAV Tracking

https://github.com/vision4robotics/sam-da

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SAM-DA: UAV Tracks Anything at Night with SAM-Powered Domain Adaptation

https://github.com/vision4robotics/tda-track

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Prompt-Driven Temporal Domain Adaptation for Nighttime UAV Tracking

https://github.com/vision4robotics/udat

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Unsupervised Domain Adaptation for Nighttime Aerial Tracking

https://github.com/visionflow/srouda_main

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SRoUDA: Meta Self-training for Robust Unsupervised Domain Adaptation

https://github.com/visionjo/facerec-bias-bfw

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Balancing Biases and Preserving Privacy on Balanced Faces in the Wild

https://github.com/visionlearninggroup/benchmark_domain_transfer

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A Broad Study of Pre-training for Domain Generalization and Adaptation

https://github.com/visionlearninggroup/snd

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Tune it the Right Way: Unsupervised Validation of Domain Adaptation via Soft Neighborhood Density

https://github.com/visionxlab/earth-adapter

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Earth-Adapter: Bridge the Geospatial Domain Gaps with Mixture of Frequency Adaptation

https://github.com/visipedia/caltech-fish-counting

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The Caltech Fish Counting Dataset: A Benchmark for Multiple-Object Tracking and Counting

https://github.com/visualizing-transferred-knowledge/vsualizing-transferred-knowledge

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Visualizing Transferred Knowledge: An Interpretive Model of Unsupervised Domain Adaptation

https://github.com/vita-epfl/looking

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Do Pedestrians Pay Attention? Eye Contact Detection in the Wild

https://github.com/vitae-transformer/aptv2

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APTv2: Benchmarking Animal Pose Estimation and Tracking with a Large-scale Dataset and Beyond

https://github.com/vitae-transformer/p3m-net

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Rethinking Portrait Matting with Privacy Preserving

https://github.com/vityavitalich/taxollama

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TaxoLLaMA: WordNet-based Model for Solving Multiple Lexical Semantic Tasks

https://github.com/vivivic/speech-domain-adaptation-DRL

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Unsupervised Speech Domain Adaptation Based on Disentangled Representation Learning for Robust Speech Recognition

https://github.com/vjosapreniqi/MoralBERT

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MoralBERT: A Fine-Tuned Language Model for Capturing Moral Values in Social Discussions

https://github.com/vkkhare/ZSL-ADA

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A Generative Framework for Zero-Shot Learning with Adversarial Domain Adaptation

https://github.com/vorobeevich/distillation-in-dg

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Weight Averaging Improves Knowledge Distillation under Domain Shift

https://github.com/vs-cv/sf-yolo

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Source-Free Domain Adaptation for YOLO Object Detection

https://github.com/vsingh-group/datasetpooling

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Equivariance Allows Handling Multiple Nuisance Variables When Analyzing Pooled Neuroimaging Datasets

https://github.com/vsydorskyy/hubmap_2022_htt_solution

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Semi-Supervised Segmentation of Functional Tissue Units at the Cellular Level

https://github.com/vsyrgkanis/orthogonal_learning

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Orthogonal Statistical Learning

https://github.com/vtddggg/Robust-Vision-Transformer

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Towards Robust Vision Transformer

https://github.com/vturrisi/udavt

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Unsupervised Domain Adaptation for Video Transformers in Action Recognition

https://github.com/w-zx-y/sm-ppm

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Style Mixing and Patchwise Prototypical Matching for One-Shot Unsupervised Domain Adaptive Semantic Segmentation

https://github.com/w1oves/rein

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Stronger, Fewer, & Superior: Harnessing Vision Foundation Models for Domain Generalized Semantic Segmentation

https://github.com/wang-ml-lab/grda

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Graph-Relational Domain Adaptation

https://github.com/wang-ml-lab/tsda

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Taxonomy-Structured Domain Adaptation

https://github.com/wang-ml-lab/vdi

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Domain-Indexing Variational Bayes: Interpretable Domain Index for Domain Adaptation

https://github.com/wang-pengfei/sagm

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Sharpness-Aware Gradient Matching for Domain Generalization

https://github.com/wangcongcong123/cast

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Crisis Domain Adaptation Using Sequence-to-sequence Transformers

https://github.com/wangguangyuan/ClusterContrast

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Cluster Contrast for Unsupervised Person Re-Identification

https://github.com/wangjiyuan9/weatherdepth

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WeatherDepth: Curriculum Contrastive Learning for Self-Supervised Depth Estimation under Adverse Weather Conditions

https://github.com/wangshanshanCQU/MCTL

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Manifold Criterion Guided Transfer Learning via Intermediate Domain Generation

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