no code implementations • CVPR 2023 • Furen Zhuang, Pierre Moulin
Metric learning requires the identification of far-apart similar pairs and close dissimilar pairs during training, and this is difficult to achieve with unlabeled data because pairs are typically assumed to be similar if they are close.
no code implementations • 8 Dec 2020 • Amish Goel, Pierre Moulin
Deep learning image classifiers are known to be vulnerable to small adversarial perturbations of input images.
no code implementations • 22 Jul 2020 • Ye Wang, Shuchin Aeron, Adnan Siraj Rakin, Toshiaki Koike-Akino, Pierre Moulin
Robust machine learning formulations have emerged to address the prevalent vulnerability of deep neural networks to adversarial examples.
no code implementations • 2 Jun 2020 • Tejas Jayashankar, Jonathan Le Roux, Pierre Moulin
Various adversarial audio attacks have recently been developed to fool automatic speech recognition (ASR) systems.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
1 code implementation • CVPR 2017 • Le Zhang, Jagannadan Varadarajan, Ponnuthurai Nagaratnam Suganthan, Narendra Ahuja, Pierre Moulin
Unlike conventional orthogonal decision trees that use a single feature and heuristic measures to obtain a split at each node, we propose to use a more powerful proximal SVM to obtain oblique hyperplanes to capture the geometric structure of the data better.
no code implementations • 1 Apr 2017 • Patrick R. Johnstone, Pierre Moulin
Thirdly we develop a novel "descending stairs" stepsize which obtains this faster convergence rate and also obtains linear convergence for the special case of weakly sharp functions.
no code implementations • 17 Mar 2016 • Honghai Yu, Pierre Moulin, Hong Wei Ng, XiaoLi Li
In particular, we propose a block K-means hashing (B-KMH) method to obtain significantly improved retrieval performance with no increase in storage and marginal increase in computational cost.
no code implementations • 8 Feb 2016 • Patrick R. Johnstone, Pierre Moulin
Our local analysis is applicable to certain recent variants of the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA), for which we establish active manifold identification and local linear convergence.
no code implementations • CVPR 2015 • Venice Erin Liong, Jiwen Lu, Gang Wang, Pierre Moulin, Jie zhou
In this paper, we propose a new deep hashing (DH) approach to learn compact binary codes for large scale visual search.
no code implementations • CVPR 2015 • Jiwen Lu, Gang Wang, Weihong Deng, Pierre Moulin, Jie zhou
In this paper, we propose a multi-manifold deep metric learning (MMDML) method for image set classification, which aims to recognize an object of interest from a set of image instances captured from varying viewpoints or under varying illuminations.
no code implementations • CVPR 2015 • Bingbing Ni, Pierre Moulin, Xiaokang Yang, Shuicheng Yan
Inspired by the recent advance in sentence regularization for text classification, we introduce a Motion Part Regularization framework to mining discriminative semi-local groups of dense trajectories.
no code implementations • CVPR 2014 • Bingbing Ni, Vignesh R. Paramathayalan, Pierre Moulin
We propose to decompose the fine-grained human activity analysis problem into two sequential tasks with increasing granularity.
no code implementations • CVPR 2014 • Bingbing Ni, Teng Li, Pierre Moulin
Specifically, for the kernel representation calculated for each input feature instance, we multiply it element-wise with a latent binary vector named as instance selection variables, which targets at selecting good instances and attenuate the effect of ambiguous ones in the resulting new kernel representation.