https://github.com/federicopozzi33/MobileOne-PyTorch
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
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.
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision
Butterfly: One-step Approach towards Wildly Unsupervised Domain Adaptation
Iterative pseudo-forced alignment by acoustic CTC loss for self-supervised ASR domain adaptation
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
On the limits of cross-domain generalization in automated X-ray prediction
Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks
Very Deep Convolutional Networks for Large-Scale Image Recognition
SuperPoint: Self-Supervised Interest Point Detection and Description
Multi-Task Deep Neural Networks for Natural Language Understanding
Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation
Domain Generalization for Object Recognition with Multi-task Autoencoders
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.