Search Results for author: Luca Rigazio

Found 10 papers, 6 papers with code

Contrastive Neural Processes for Self-Supervised Learning

1 code implementation24 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.

Contrastive Learning Data Augmentation +2

AutoDO: Robust AutoAugment for Biased Data with Label Noise via Scalable Probabilistic Implicit Differentiation

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.

DoorGym: A Scalable Door Opening Environment And Baseline Agent

1 code implementation5 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.

DNN Feature Map Compression using Learned Representation over GF(2)

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.

Dimensionality Reduction General Classification +3

ShiftCNN: Generalized Low-Precision Architecture for Inference of Convolutional Neural Networks

1 code implementation7 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).

Quantization

Similarity Mapping with Enhanced Siamese Network for Multi-Object Tracking

no code implementations28 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).

Multi-Object Tracking

Deep Clustered Convolutional Kernels

no code implementations6 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.

Towards Deep Neural Network Architectures Robust to Adversarial Examples

2 code implementations11 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).

Denoising

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