Domain Adaptation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 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:
<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/cornell-zhang/allo-pldi24-artifact
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/crazydigger/Callibration-of-GPT
https://github.com/criteo-research/pytorch-ada
https://github.com/crownx/spa
SPA: A Graph Spectral Alignment Perspective for Domain Adaptation
https://github.com/cryu854/ArbitraryStyle
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/cryu854/ArbitraryStyle-tfjs
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/cryu854/CUT
https://github.com/cufctl/segmentation
https://github.com/cuis15/FCFL
https://github.com/cuixing100876/instastyle
Learning Disentangled Prompts for Compositional Image Synthesis
https://github.com/cvqluu/GE2E-Loss
https://github.com/cyvius96/few-shot-meta-baseline
https://github.com/daimonae/Fundus-Image-Classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/dalonlobo/diarization-experiments
https://github.com/damo-cv/TransReID-SSL
Person Transfer GAN to Bridge Domain Gap for Person Re-Identification
https://github.com/danielamassiceti/CCA-visualdialogue
https://github.com/danielparada1/RL_SpaceInvaders
https://github.com/danielpatrickhug/Research_Paper_Parser
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/dansuh17/alexnet-pytorch
https://github.com/daoyuan98/MSDA
Multiple Source Domain Adaptation with Adversarial Training of Neural Networks
https://github.com/darrishabh/coviprox
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/darsh10/qra_code
Adversarial Domain Adaptation for Duplicate Question Detection
https://github.com/darya-baranovskaya/keyword_spotting
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/datawiz1984/MLND_Capstone_Project
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/datawiz1984/malaria-model
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/datoboat/Vehicle-Detection
https://github.com/davidpengucf/RAIN
RAIN: RegulArization on Input and Network for Black-Box Domain Adaptation
https://github.com/davisking/dlib-models
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/dead-mazai/avia-missing-airplanes
https://github.com/dectrfov/NTIRE-2021-Depth-Guided-Image-Any-to-Any-relighting
https://github.com/deepakprabakar96/DTN
https://github.com/deeplearning-wisc/hypo
https://github.com/deepmind/dm-haiku
https://github.com/deepmodeling/deepmd-kit
https://github.com/deepquestai/fire-smoke-dataset
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.