361 citations · 944 across the 48 of their papers we have counts for
14 papers · 1 filter
Scalable and Compact 3D Action Recognition with Approximated RBF Kernel Machines
Jacopo Cavazza, Pietro Morerio, Vittorio Murino
Despite the recent deep learning (DL) revolution, kernel machines still remain powerful methods for action recognition. DL has brought the use of large datasets and this is typical…
Minimal-Entropy Correlation Alignment for Unsupervised Deep Domain Adaptation
Pietro Morerio, Jacopo Cavazza, Vittorio Murino
In this work, we face the problem of unsupervised domain adaptation with a novel deep learning approach which leverages on our finding that entropy minimization is induced by the o…
Adversarial Feature Augmentation for Unsupervised Domain Adaptation
Riccardo Volpi, Pietro Morerio, Silvio Savarese +1
Recent works showed that Generative Adversarial Networks (GANs) can be successfully applied in unsupervised domain adaptation, where, given a labeled source dataset and an unlabele…
Excitation Backprop for RNNs
Sarah Adel Bargal, Andrea Zunino, Donghyun Kim +3
Deep models are state-of-the-art for many vision tasks including video action recognition and video captioning. Models are trained to caption or classify activity in videos, but li…
Dropout as a Low-Rank Regularizer for Matrix Factorization
Jacopo Cavazza, Pietro Morerio, Benjamin Haeffele +3
Regularization for matrix factorization (MF) and approximation problems has been carried out in many different ways. Due to its popularity in deep learning, dropout has been applie…
An Analysis of Dropout for Matrix Factorization
Jacopo Cavazza, Connor Lane, Benjamin D. Haeffele +2
Dropout is a simple yet effective algorithm for regularizing neural networks by randomly dropping out units through Bernoulli multiplicative noise, and for some restricted problem…