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Found 5 papers, 3 papers with code
ProbMCL: Simple Probabilistic Contrastive Learning for Multi-label Visual Classification
We validate the effectiveness of our framework through experimentation with datasets from the computer vision and medical imaging domains.
DataDAM: Efficient Dataset Distillation with Attention Matching
Emerging research on dataset distillation aims to reduce training costs by creating a small synthetic set that contains the information of a larger real dataset and ultimately achieves test accuracy equivalent to a model trained on the whole dataset.
A New Probabilistic Distance Metric With Application In Gaussian Mixture Reduction
This paper presents a new distance metric to compare two continuous probability density functions.
Subclass Knowledge Distillation with Known Subclass Labels
In classification tasks with a small number of classes or binary detection, the amount of information transferred from the teacher to the student is restricted, thus limiting the utility of knowledge distillation.
Maximum Mutation Reinforcement Learning for Scalable Control
Advances in Reinforcement Learning (RL) have demonstrated data efficiency and optimal control over large state spaces at the cost of scalable performance.
