2 citations · 4 across the 5 of their papers we have counts for
10 papers
The Dynamic of Consensus in Deep Networks and the Identification of Noisy Labels
Daniel Shwartz, Uri Stern, Daphna Weinshall
Deep neural networks have incredible capacity and expressibility, and can seemingly memorize any training set. This introduces a problem when training in the presence of noisy labe…
More Is More -- Narrowing the Generalization Gap by Adding Classification Heads
Roee Cates, Daphna Weinshall
Overfit is a fundamental problem in machine learning in general, and in deep learning in particular. In order to reduce overfit and improve generalization in the classification of…
Boosting the Performance of Semi-Supervised Learning with Unsupervised Clustering
Boaz Lerner, Guy Shiran, Daphna Weinshall
Recently, Semi-Supervised Learning (SSL) has shown much promise in leveraging unlabeled data while being provided with very few labels. In this paper, we show that ignoring the lab…
Multiclass non-Adversarial Image Synthesis, with Application to Classification from Very Small Sample
Itamar Winter, Daphna Weinshall
The generation of synthetic images is currently being dominated by Generative Adversarial Networks (GANs). Despite their outstanding success in generating realistic looking images,…
Generative Latent Implicit Conditional Optimization when Learning from Small Sample
Idan Azuri, Daphna Weinshall
We revisit the long-standing problem of learning from a small sample, to which end we propose a novel method called GLICO (Generative Latent Implicit Conditional Optimization). GLI…
Multi-Modal Deep Clustering: Unsupervised Partitioning of Images
Guy Shiran, Daphna Weinshall
The clustering of unlabeled raw images is a daunting task, which has recently been approached with some success by deep learning methods. Here we propose an unsupervised clustering…