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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/syscv/ciss

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Condition-Invariant Semantic Segmentation

https://github.com/sz128/DAAT_SLU

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Data Augmentation with Atomic Templates for Spoken Language Understanding

https://github.com/szc12153/sparse_meta_tuning

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Unleashing the Power of Meta-tuning for Few-shot Generalization Through Sparse Interpolated Experts

https://github.com/szubing/uniood

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Universal Domain Adaptation from Foundation Models: A Baseline Study

https://github.com/t6-thu/xTED

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xTED: Cross-Domain Adaptation via Diffusion-Based Trajectory Editing

https://github.com/taeckyung/SoTTA

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SoTTA: Robust Test-Time Adaptation on Noisy Data Streams

https://github.com/taeckyung/aetta

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AETTA: Label-Free Accuracy Estimation for Test-Time Adaptation

https://github.com/taeckyung/bitta

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Test-Time Adaptation with Binary Feedback

https://github.com/taesikgong/note

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NOTE: Robust Continual Test-time Adaptation Against Temporal Correlation

https://github.com/tahmedge/preqfas

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Domain Adaptation with Pre-trained Transformers for Query Focused Abstractive Text Summarization

https://github.com/taimurhassan/continual_learning

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Incremental Cross-Domain Adaptation for Robust Retinopathy Screening via Bayesian Deep Learning

https://github.com/takahikof/masklrf

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MaskLRF: Self-supervised Pretraining via Masked Autoencoding of Local Reference Frames for Rotation-invariant 3D Point Set Analysis

https://github.com/takeshi-teshima/few-shot-domain-adaptation-by-causal-mechanism-transfer

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Few-shot Domain Adaptation by Causal Mechanism Transfer

https://github.com/tangzhenjie/SRDA-Net

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Super-Resolution Domain Adaptation Networks for Semantic Segmentation via Pixel and Output Level Aligning

https://github.com/tanodino/sshda

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Semi Supervised Heterogeneous Domain Adaptation via Disentanglement and Pseudo-Labelling

https://github.com/taohan10200/NLT

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Neuron Linear Transformation: Modeling the Domain Shift for Crowd Counting

https://github.com/taolinzhang/boostadapter

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BoostAdapter: Improving Vision-Language Test-Time Adaptation via Regional Bootstrapping

https://github.com/taoruijie/ecapatdnn

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HLT-NUS SUBMISSION FOR 2020 NIST Conversational Telephone Speech SRE

https://github.com/tareknaous/readme

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ReadMe++: Benchmarking Multilingual Language Models for Multi-Domain Readability Assessment

https://github.com/tartunlp/inherent-domains-wmt21

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Translation Transformers Rediscover Inherent Data Domains

https://github.com/tarun005/USSS_ICCV19

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Universal Semi-Supervised Semantic Segmentation

https://github.com/taveraantonio/rtda

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Reimagine BiSeNet for Real-Time Domain Adaptation in Semantic Segmentation

https://github.com/tayebiarasteh/fldomain

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Mind the Gap: Federated Learning Broadens Domain Generalization in Diagnostic AI Models

https://github.com/tayebiarasteh/privacydomain

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Preserving privacy in domain transfer of medical AI models comes at no performance costs: The integral role of differential privacy

https://github.com/taylover-pei/SSDG-CVPR2020

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Single-Side Domain Generalization for Face Anti-Spoofing

https://github.com/teaghan/Cycle_SN

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Cycle-StarNet: Bridging the gap between theory and data by leveraging large datasets

https://github.com/technion-cs-nlp/idani

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IDANI: Inference-time Domain Adaptation via Neuron-level Interventions

https://github.com/tejas-gokhale/alt

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Improving Diversity with Adversarially Learned Transformations for Domain Generalization

https://github.com/tencentyouturesearch/activelearning-sdm

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Learning Distinctive Margin toward Active Domain Adaptation

https://github.com/tensorflow/models

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Domain Separation Networks

https://github.com/tensorflow/tpu

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Adversarial Examples Improve Image Recognition

https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet

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EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/teresa-sc/concepts_in_vits

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From Colors to Classes: Emergence of Concepts in Vision Transformers

https://github.com/tfboys-lzz/mpscl

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Margin Preserving Self-paced Contrastive Learning Towards Domain Adaptation for Medical Image Segmentation

https://github.com/thakur-nandan/income

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Injecting Domain Adaptation with Learning-to-hash for Effective and Efficient Zero-shot Dense Retrieval

https://github.com/theeighthday/co-teaching

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Co-Teaching for Unsupervised Domain Adaptation and Expansion

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