https://github.com/tzvikif/SuperGlue
SuperPoint: Self-Supervised Interest Point Detection and Description
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.
SuperPoint: Self-Supervised Interest Point Detection and Description
Overcoming Data and Model Heterogeneities in Decentralized Federated Learning via Synthetic Anchors
Person Transfer GAN to Bridge Domain Gap for Person Re-Identification
Very Deep Convolutional Networks for Large-Scale Image Recognition
SuperPoint: Self-Supervised Interest Point Detection and Description
RemixIT: Continual self-training of speech enhancement models via bootstrapped remixing
The Effect of Domain and Diacritics in Yorùbá-English Neural Machine Translation
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
X-FACT: A New Benchmark Dataset for Multilingual Fact Checking
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
ESL: Entropy-guided Self-supervised Learning for Domain Adaptation in Semantic Segmentation
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
SuperPoint: Self-Supervised Interest Point Detection and Description
SuperPoint: Self-Supervised Interest Point Detection and Description
Very Deep Convolutional Networks for Large-Scale Image Recognition
Virtual to Real Reinforcement Learning for Autonomous Driving
XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.