https://github.com/megvii-research/basecls/tree/main/zoo/public/vgg
Very Deep Convolutional Networks for Large-Scale Image Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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>
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Very Deep Convolutional Networks for Large-Scale Image Recognition
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Very Deep Convolutional Networks for Large-Scale Image Recognition
Priming Cross-Session Motor Imagery Classification with A Universal Deep Domain Adaptation Framework
Priming Cross-Session Motor Imagery Classification with A Universal Deep Domain Adaptation Framework
Priming Cross-Session Motor Imagery Classification with A Universal Deep Domain Adaptation Framework
Diffusion-TTA: Test-time Adaptation of Discriminative Models via Generative Feedback
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Aggregated Residual Transformations for Deep Neural Networks
Very Deep Convolutional Networks for Large-Scale Image Recognition
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Semi-supervised Domain Adaptation based on Dual-level Domain Mixing for Semantic Segmentation
Aggregated Residual Transformations for Deep Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.