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QUEST: Quantized embedding space for transferring knowledge
Himalaya Jain, Spyros Gidaris, Nikos Komodakis +2
Knowledge distillation refers to the process of training a compact student network to achieve better accuracy by learning from a high capacity teacher network. Most of the existing…
Deep Tone Mapping Operator for High Dynamic Range Images
Aakanksha Rana, Praveer Singh, Giuseppe Valenzise +3
A computationally fast tone mapping operator (TMO) that can quickly adapt to a wide spectrum of high dynamic range (HDR) content is quintessential for visualization on varied low d…
Boosting Few-Shot Visual Learning with Self-Supervision
Spyros Gidaris, Andrei Bursuc, Nikos Komodakis +2
Few-shot learning and self-supervised learning address different facets of the same problem: how to train a model with little or no labeled data. Few-shot learning aims for optimiz…
Generating Classification Weights with GNN Denoising Autoencoders for Few-Shot Learning
Spyros Gidaris, Nikos Komodakis
Given an initial recognition model already trained on a set of base classes, the goal of this work is to develop a meta-model for few-shot learning. The meta-model, given as input…
Exploring Weight Symmetry in Deep Neural Networks
Xu Shell Hu, Sergey Zagoruyko, Nikos Komodakis
We propose to impose symmetry in neural network parameters to improve parameter usage and make use of dedicated convolution and matrix multiplication routines. Due to significant r…