Search Results for author:
Found 3 papers, 0 papers with code
MaskMatch: Boosting Semi-Supervised Learning Through Mask Autoencoder-Driven Feature Learning
To overcome this limitation and improve SSL performance, we introduce \algo, a novel algorithm that fully utilizes unlabeled data to boost semi-supervised learning.
HODEC: Towards Efficient High-Order DEcomposed Convolutional Neural Networks
High-order decomposition is a widely used model compression approach towards compact convolutional neural networks (CNNs).
SPARK: co-exploring model SPArsity and low-RanKness for compact neural networks
Based on the observations and outcomes from our analysis, we then propose SPARK, a unified DNN compression framework that can simultaneously capture model SPArsity and low-RanKness in an efficient way.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
