https://github.com/deftruth/torchlm
Pixel-in-Pixel Net: Towards Efficient Facial Landmark Detection in the Wild
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.
Pixel-in-Pixel Net: Towards Efficient Facial Landmark Detection in the Wild
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Aggregated Residual Transformations for Deep Neural Networks
Multiple Source Domain Adaptation with Adversarial Training of Neural Networks
Improved Regularization of Convolutional Neural Networks with Cutout
Very Deep Convolutional Networks for Large-Scale Image Recognition
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Very Deep Convolutional Networks for Large-Scale Image Recognition
PatchNet: A Simple Face Anti-Spoofing Framework via Fine-Grained Patch Recognition
Benchmarking Robustness of 3D Point Cloud Recognition Against Common Corruptions
Benchmarking Robustness of 3D Point Cloud Recognition Against Common Corruptions
Racial Faces in-the-Wild: Reducing Racial Bias by Information Maximization Adaptation Network
Unsupervised Multi-source Domain Adaptation Without Access to Source Data
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.