33 citations · 117 across the 8 of their papers we have counts for
8 papers
Maximally Compact and Separated Features with Regular Polytope Networks
Federico Pernici, Matteo Bruni, Claudio Baecchi +1
Convolutional Neural Networks (CNNs) trained with the Softmax loss are widely used classification models for several vision tasks. Typically, a learnable transformation (i.e. the c…
CL2R: Compatible Lifelong Learning Representations
Niccolo Biondi, Federico Pernici, Matteo Bruni +2
In this paper, we propose a method to partially mimic natural intelligence for the problem of lifelong learning representations that are compatible. We take the perspective of a le…
Contrastive Supervised Distillation for Continual Representation Learning
Tommaso Barletti, Niccolo' Biondi, Federico Pernici +2
In this paper, we propose a novel training procedure for the continual representation learning problem in which a neural network model is sequentially learned to alleviate catastro…
CoReS: Compatible Representations via Stationarity
Niccolo Biondi, Federico Pernici, Matteo Bruni +1
Compatible features enable the direct comparison of old and new learned features allowing to use them interchangeably over time. In visual search systems, this eliminates the need…
Regular Polytope Networks
Federico Pernici, Matteo Bruni, Claudio Baecchi +1
Neural networks are widely used as a model for classification in a large variety of tasks. Typically, a learnable transformation (i.e. the classifier) is placed at the end of such…
Class-incremental Learning with Pre-allocated Fixed Classifiers
Federico Pernici, Matteo Bruni, Claudio Baecchi +2
In class-incremental learning, a learning agent faces a stream of data with the goal of learning new classes while not forgetting previous ones. Neural networks are known to suffer…
Fix Your Features: Stationary and Maximally Discriminative Embeddings using Regular Polytope (Fixed Classifier) Networks
Federico Pernici, Matteo Bruni, Claudio Baecchi +1
Neural networks are widely used as a model for classification in a large variety of tasks. Typically, a learnable transformation (i.e. the classifier) is placed at the end of such…
Memory Based Online Learning of Deep Representations from Video Streams
Federico Pernici, Federico Bartoli, Matteo Bruni +1
We present a novel online unsupervised method for face identity learning from video streams. The method exploits deep face descriptors together with a memory based learning mechani…