64 citations · 130 across the 6 of their papers we have counts for
6 papers
It's All in the Head: Representation Knowledge Distillation through Classifier Sharing
Emanuel Ben-Baruch, Matan Karklinsky, Yossi Biton +3
Representation knowledge distillation aims at transferring rich information from one model to another. Common approaches for representation distillation mainly focus on the direct…
Multi-label Classification with Partial Annotations using Class-aware Selective Loss
Emanuel Ben-Baruch, Tal Ridnik, Itamar Friedman +4
Large-scale multi-label classification datasets are commonly, and perhaps inevitably, partially annotated. That is, only a small subset of labels are annotated per sample. Differen…
PETA: Photo Albums Event Recognition using Transformers Attention
Tamar Glaser, Emanuel Ben-Baruch, Gilad Sharir +3
In recent years the amounts of personal photos captured increased significantly, giving rise to new challenges in multi-image understanding and high-level image understanding. Even…
Semantic Diversity Learning for Zero-Shot Multi-label Classification
Avi Ben-Cohen, Nadav Zamir, Emanuel Ben Baruch +2
Training a neural network model for recognizing multiple labels associated with an image, including identifying unseen labels, is challenging, especially for images that portray nu…
Asymmetric Loss For Multi-Label Classification
Emanuel Ben-Baruch, Tal Ridnik, Nadav Zamir +4
In a typical multi-label setting, a picture contains on average few positive labels, and many negative ones. This positive-negative imbalance dominates the optimization process, an…
ASAP: Architecture Search, Anneal and Prune
Asaf Noy, Niv Nayman, Tal Ridnik +5
Automatic methods for Neural Architecture Search (NAS) have been shown to produce state-of-the-art network models. Yet, their main drawback is the computational complexity of the s…