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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/mkashifali/metavideostab

공식 코드

Harnessing Meta-Learning for Improving Full-Frame Video Stabilization

https://github.com/mkirchmeyer/adaptation-imputation

공식 코드

Unsupervised domain adaptation with non-stochastic missing data

https://github.com/mkirchmeyer/ostar

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Mapping conditional distributions for domain adaptation under generalized target shift

https://github.com/mklimasz/transferable-modularity

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Is Modularity Transferable? A Case Study through the Lens of Knowledge Distillation

https://github.com/mkmenta/domain_adapt_segm

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Learning to adapt class-specific features across domains for semantic segmentation

https://github.com/mkuzma96/mtrnet

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Estimating Conditional Average Treatment Effects with Missing Treatment Information

https://github.com/ml4ai/mliis

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Meta-Learning Initializations for Image Segmentation

https://github.com/mlaricheva/automated_labeling

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Automated Utterance Labeling of Conversations Using Natural Language Processing

https://github.com/mlfoundations/model-soups

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Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time

https://github.com/mlfoundations/tableshift

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Benchmarking Distribution Shift in Tabular Data with TableShift

https://github.com/mli-lab/ttt_for_deep_learning_cs

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Test-Time Training Can Close the Natural Distribution Shift Performance Gap in Deep Learning Based Compressed Sensing

https://github.com/mlmed/torchxrayvision

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On the limits of cross-domain generalization in automated X-ray prediction

https://github.com/mlnotebook/domain_adapation_istn

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Image-level Harmonization of Multi-Site Data using Image-and-Spatial Transformer Networks

https://github.com/mlvlab/prometar

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Prompt Learning via Meta-Regularization

https://github.com/mlvlab/rpo

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Read-only Prompt Optimization for Vision-Language Few-shot Learning

https://github.com/mmahbub/bioadapt-mrc

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BioADAPT-MRC: Adversarial Learning-based Domain Adaptation Improves Biomedical Machine Reading Comprehension Task

https://github.com/mmatena/model_merging

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Merging Models with Fisher-Weighted Averaging

https://github.com/mmjing/ocr

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Order-preserving Consistency Regularization for Domain Adaptation and Generalization

https://github.com/mmjing/variational_model_perturbation

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Variational Model Perturbation for Source-Free Domain Adaptation

https://github.com/mnschmit/unsupervised-graph-text-conversion

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An Unsupervised Joint System for Text Generation from Knowledge Graphs and Semantic Parsing

https://github.com/modeeric/orbit-llama

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ORBIT: Cost-Effective Dataset Curation for Large Language Model Domain Adaptation with an Astronomy Case Study

https://github.com/modestyachts/imagenet-testbed

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Measuring Robustness to Natural Distribution Shifts in Image Classification

https://github.com/moepien/sliced_ot_for_gmms

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Slicing the Gaussian Mixture Wasserstein Distance

https://github.com/mohamedr002/mapu_sfda_ts

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Source-Free Domain Adaptation with Temporal Imputation for Time Series Data

https://github.com/mohamedr002/slarda

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Self-supervised Autoregressive Domain Adaptation for Time Series Data

https://github.com/mohamedtev/daca

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Detect, Augment, Compose, and Adapt: Four Steps for Unsupervised Domain Adaptation in Object Detection

https://github.com/mohammadrezaebrahimi/handa

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Heterogeneous Domain Adaptation with Adversarial Neural Representation Learning: Experiments on E-Commerce and Cybersecurity

https://github.com/morningmoni/citesum

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CiteSum: Citation Text-guided Scientific Extreme Summarization and Domain Adaptation with Limited Supervision

https://github.com/mostafajahanifar/histodomainbed

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Benchmarking Domain Generalization Algorithms in Computational Pathology

https://github.com/motasemalfarra/online_test_time_adaptation

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Evaluation of Test-Time Adaptation Under Computational Time Constraints

https://github.com/mperezcarrasco/Con2DA

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Con$^{2}$DA: Simplifying Semi-supervised Domain Adaptation by Learning Consistent and Contrastive Feature Representations

https://github.com/mpilligua/layereddoc

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LayeredDoc: Domain Adaptive Document Restoration with a Layer Separation Approach

https://github.com/mqinghe/midss

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Constructing and Exploring Intermediate Domains in Mixed Domain Semi-supervised Medical Image Segmentation

https://github.com/mqinghe/ust-run

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Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation

https://github.com/mr-eggplant/eata

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Efficient Test-Time Model Adaptation without Forgetting

https://github.com/mr-eggplant/foa

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Test-Time Model Adaptation with Only Forward Passes

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