Self-Learning
88 papers with code • 0 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Self-Learning
LEARNING STYLE IDENTIFICATION
Most implemented papers
MixSpeech: Cross-Modality Self-Learning with Audio-Visual Stream Mixup for Visual Speech Translation and Recognition
However, despite researchers exploring cross-lingual translation techniques such as machine translation and audio speech translation to overcome language barriers, there is still a shortage of cross-lingual studies on visual speech.
Point-supervised Single-cell Segmentation via Collaborative Knowledge Sharing
jiyuuchc/lacss
•
•
This strategy achieves self-learning by sharing knowledge between a principal model and a very light-weight collaborator model.
Self-Learning Cloud Controllers: Fuzzy Q-Learning for Knowledge Evolution
The benefit is that for designing cloud controllers, we do not have to rely solely on precise design-time knowledge, which may be difficult to acquire.
End2You -- The Imperial Toolkit for Multimodal Profiling by End-to-End Learning
end2you/end2you
•
•
To our knowledge, this is the first toolkit that provides generic end-to-end learning for profiling capabilities in either unimodal or multimodal cases.
A Deep Q-Learning Agent for the L-Game with Variable Batch Training
We employ the Deep Q-Learning algorithm with Experience Replay to train an agent capable of achieving a high-level of play in the L-Game while self-learning from low-dimensional states.
General solutions for nonlinear differential equations: a rule-based self-learning approach using deep reinforcement learning
A universal rule-based self-learning approach using deep reinforcement learning (DRL) is proposed for the first time to solve nonlinear ordinary differential equations and partial differential equations.
Market Self-Learning of Signals, Impact and Optimal Trading: Invisible Hand Inference with Free Energy
In particular, it represents, in a simple modeling framework, market views of common predictive signals, market impacts and implied optimal dynamic portfolio allocations, and can be used to assess values of private signals.
Cost-effective Object Detection: Active Sample Mining with Switchable Selection Criteria
yanxp/ASM
•
•
The proposed process can be compatible with mini-batch based training (i. e., using a batch of unlabeled or partially labeled data as a one-time input) for object detection.
Model-Free Adaptive Optimal Control of Episodic Fixed-Horizon Manufacturing Processes using Reinforcement Learning
A self-learning optimal control algorithm for episodic fixed-horizon manufacturing processes with time-discrete control actions is proposed and evaluated on a simulated deep drawing process.
CVABS: Moving Object Segmentation with Common Vector Approach for Videos
isahhin/cvabs
•
•
Background modelling is a fundamental step for several real-time computer vision applications that requires security systems and monitoring.
