Papers with Code 아카이브
One-Shot Learning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 1
- 보존된 평가 기록
- 1
One-shot learning is the task of learning information about object categories from a single training example.
<span style="color:grey; opacity: 0.6">( Image credit: Siamese Neural Networks for One-shot Image Recognition )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Active One-shot Learning
Discovering objects and their relations from entangled scene representations
Fast Adaptation in Generative Models with Generative Matching Networks
Inverting The Generator Of A Generative Adversarial Network
Low Data Drug Discovery with One-shot Learning
Latent Attention For If-Then Program Synthesis
ARTiS: Appearance-based Action Recognition in Task Space for Real-Time Human-Robot Collaboration
Task Specific Adversarial Cost Function
Learning to learn with backpropagation of Hebbian plasticity
Learning feed-forward one-shot learners
Covariance of Motion and Appearance Featuresfor Spatio Temporal Recognition Tasks
Matching Networks for One Shot Learning
One-shot Learning with Memory-Augmented Neural Networks
Modeling Time Series Similarity with Siamese Recurrent Networks
One-Shot Learning of Manipulation Skills with Online Dynamics Adaptation and Neural Network Priors
Learning Non-deterministic Representations with Energy-based Ensembles
One-Shot-Learning Gesture Recognition using HOG-HOF Features
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.