Search Results for author:
Found 2 papers, 1 papers with code
CL2R: Compatible Lifelong Learning Representations
We identify stationarity as the property that the feature representation is required to hold to achieve compatibility and propose a novel training procedure that encourages local and global stationarity on the learned representation.
Fine-Grained Adversarial Semi-supervised Learning
Our approach leverages unlabeled data with an adversarial optimization strategy in which the internal features representation is obtained with a second-order pooling model.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
