Search Results for author: Ronan Sicre

Found 7 papers, 3 papers with code

Opti-CAM: Optimizing saliency maps for interpretability

no code implementations17 Jan 2023 Hanwei Zhang, Felipe Torres, Ronan Sicre, Yannis Avrithis, Stephane Ayache

Methods based on class activation maps (CAM) provide a simple mechanism to interpret predictions of convolutional neural networks by using linear combinations of feature maps as saliency maps.

Towards Good Practices in Evaluating Transfer Adversarial Attacks

1 code implementation17 Nov 2022 Zhengyu Zhao, Hanwei Zhang, Renjue Li, Ronan Sicre, Laurent Amsaleg, Michael Backes

In this work, we design good practices to address these limitations, and we present the first comprehensive evaluation of transfer attacks, covering 23 representative attacks against 9 defenses on ImageNet.

Sparse matrix products for neural network compression

no code implementations1 Jan 2021 Luc Giffon, Hachem Kadri, Stephane Ayache, Ronan Sicre, Thierry Artieres

Over-parameterization of neural networks is a well known issue that comes along with their great performance.

Neural Network Compression

Unsupervised part learning for visual recognition

no code implementations CVPR 2017 Ronan Sicre, Yannis Avrithis, Ewa Kijak, Frederic Jurie

This strategy opens the door to the use of PBM in new applications for which the notion of image categories is irrelevant, such as instance-based image retrieval, for example.

Classification General Classification +3

Automatic discovery of discriminative parts as a quadratic assignment problem

no code implementations14 Nov 2016 Ronan Sicre, Julien Rabin, Yannis Avrithis, Teddy Furon, Frederic Jurie

Part-based image classification consists in representing categories by small sets of discriminative parts upon which a representation of the images is built.

General Classification Image Classification

Particular object retrieval with integral max-pooling of CNN activations

6 code implementations18 Nov 2015 Giorgos Tolias, Ronan Sicre, Hervé Jégou

Recently, image representation built upon Convolutional Neural Network (CNN) has been shown to provide effective descriptors for image search, outperforming pre-CNN features as short-vector representations.

Image Retrieval Re-Ranking +1

Cannot find the paper you are looking for? You can Submit a new open access paper.