1 code implementation • 24 Oct 2021 • Konstantinos Kallidromitis, Denis Gudovskiy, Kazuki Kozuka, Iku Ohama, Luca Rigazio
In this paper, we propose a novel self-supervised learning framework that combines contrastive learning with neural processes.
1 code implementation • CVPR 2021 • Denis Gudovskiy, Luca Rigazio, Shun Ishizaka, Kazuki Kozuka, Sotaro Tsukizawa
To overcome these limitations, we reformulate AutoAugment as a generalized automated dataset optimization (AutoDO) task that minimizes the distribution shift between test data and distorted train dataset.
1 code implementation • 5 Aug 2019 • Yusuke Urakami, Alec Hodgkinson, Casey Carlin, Randall Leu, Luca Rigazio, Pieter Abbeel
We introduce DoorGym, an open-source door opening simulation framework designed to utilize domain randomization to train a stable policy.
1 code implementation • ICLR 2018 • Denis A. Gudovskiy, Alec Hodgkinson, Luca Rigazio
In this paper, we introduce a method to compress intermediate feature maps of deep neural networks (DNNs) to decrease memory storage and bandwidth requirements during inference.
no code implementations • 29 Jun 2017 • Masashi Okada, Luca Rigazio, Takenobu Aoshima
We also show that PI-Net is able to learn dynamics and cost models latent in the demonstrations.
1 code implementation • 7 Jun 2017 • Denis A. Gudovskiy, Luca Rigazio
In this paper we introduce ShiftCNN, a generalized low-precision architecture for inference of multiplierless convolutional neural networks (CNNs).
no code implementations • 1 Jun 2017 • Stefano Alletto, Davide Abati, Simone Calderara, Rita Cucchiara, Luca Rigazio
We address unsupervised optical flow estimation for ego-centric motion.
no code implementations • 28 Sep 2016 • Minyoung Kim, Stefano Alletto, Luca Rigazio
Multi-object tracking has recently become an important area of computer vision, especially for Advanced Driver Assistance Systems (ADAS).
no code implementations • 6 Mar 2015 • Minyoung Kim, Luca Rigazio
Deep neural networks have recently achieved state of the art performance thanks to new training algorithms for rapid parameter estimation and new regularization methods to reduce overfitting.
2 code implementations • 11 Dec 2014 • Shixiang Gu, Luca Rigazio
We perform various experiments to assess the removability of adversarial examples by corrupting with additional noise and pre-processing with denoising autoencoders (DAEs).