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Found 6 papers, 3 papers with code
Interpolation-based semi-supervised learning for object detection
Despite the data labeling cost for the object detection tasks being substantially more than that of the classification tasks, semi-supervised learning methods for object detection have not been studied much.
Class-Imbalanced Semi-Supervised Learning
First, we analyze existing SSL methods in imbalanced environments and examine how the class imbalance affects SSL methods.
Feature Fusion for Online Mutual Knowledge Distillation
We propose a learning framework named Feature Fusion Learning (FFL) that efficiently trains a powerful classifier through a fusion module which combines the feature maps generated from parallel neural networks.
Disentangling Options with Hellinger Distance Regularizer
In reinforcement learning (RL), temporal abstraction still remains as an important and unsolved problem.
Task-oriented Design through Deep Reinforcement Learning
We propose a new low-cost machine-learning-based methodology which assists designers in reducing the gap between the problem and the solution in the design process.
Towards Governing Agent's Efficacy: Action-Conditional $β$-VAE for Deep Transparent Reinforcement Learning
We tackle the blackbox issue of deep neural networks in the settings of reinforcement learning (RL) where neural agents learn towards maximizing reward gains in an uncontrollable way.
