Papers with Code 아카이브
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/SHI-Labs/Compact-Transformers
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/SJTU-Thinklab-Det/r3det-on-mmdetection
https://github.com/SRDAMICCAI/Source-Relaxed-Domain-Adaptation
https://github.com/SSN15/Traffic-sign-recognition-using-deep-learning-and-computer-vision
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Sakib1263/1DResNet-Builder-KERAS
https://github.com/Sakib1263/1DResNet-KERAS
https://github.com/Sakib1263/DenseNet-1D-2D-Tensorflow-Keras
https://github.com/Sakib1263/ResNet-Model-Builder-KERAS
https://github.com/Sakib1263/ResNet-Model-Builder-Tensorflow-Keras
https://github.com/Sakib1263/ResNet-ResNeXt-1D-2D-Tensorflow-Keras
https://github.com/Sakib1263/ResNet-ResNetv2-ResNeXt-1D-2D-Tensorflow-Keras
https://github.com/Sakib1263/ResNet1D-Model-Builder-KERAS
https://github.com/Sakib1263/VGG-1D-2D-Tensorflow-Keras
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Sakib1263/VGG-Model-Builder-KERAS
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Sakib1263/VGG-Model-Builder-Tensorflow-Keras
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/SamsungSAILMontreal/ForestDiffusion
https://github.com/Samyu0304/thought-propagation
https://github.com/SanaAwan5/UFDA1
Multiple Source Domain Adaptation with Adversarial Training of Neural Networks
https://github.com/Sangkwun/Resnet
https://github.com/SarthakGarg13/OTOMYCOSIS
https://github.com/SarthakYadav/audax
https://github.com/Schuck9/CoDats-For-Fault-diagnosis
Multi-Source Deep Domain Adaptation with Weak Supervision for Time-Series Sensor Data
https://github.com/SeniorDev009/ONNX-project
https://github.com/SethEBaldwin/Resnet
https://github.com/SforAiDl/vformer
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/Shawey94/NoisyNN
Feature Fusion Transferability Aware Transformer for Unsupervised Domain Adaptation
https://github.com/ShawnNew/shape-alpha-matting
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ShichengChen/Domain-Adversarial-Training-of-Neural-Networks
https://github.com/ShichengChen/WaveNetSeparateAudio
https://github.com/ShigemichiMatsuzaki/MSPL
https://github.com/Shivamt99/Capstone_Project
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Shorouq-Aliyan/classification
https://github.com/Shubhammawa/MixMatch-Semi-Supervised-Learning
https://github.com/ShuvenduRoy/FER_TL_PipelineTraining
Consistency-guided Prompt Learning for Vision-Language Models
https://github.com/SiHaoShen/Convolutional-Neural-Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.