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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…
Large-Scale Historical Watermark Recognition: dataset and a new consistency-based approach
Xi Shen, Ilaria Pastrolin, Oumayma Bounou +4
Historical watermark recognition is a highly practical, yet unsolved challenge for archivists and historians. With a large number of well-defined classes, cluttered and noisy sampl…
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…