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Found 13 papers, 1 papers with code
Deep Semi-Supervised Metric Learning With Mixed Label Propagation
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.
Locally optimal detection of stochastic targeted universal adversarial perturbations
Deep learning image classifiers are known to be vulnerable to small adversarial perturbations of input images.
Robust Machine Learning via Privacy/Rate-Distortion Theory
Robust machine learning formulations have emerged to address the prevalent vulnerability of deep neural networks to adversarial examples.
Detecting Audio Attacks on ASR Systems with Dropout Uncertainty
Various adversarial audio attacks have recently been developed to fool automatic speech recognition (ASR) systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Robust Visual Tracking Using Oblique Random Forests
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.
Faster Subgradient Methods for Functions with Hölderian Growth
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.
Variable-Length Hashing
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.
Local and Global Convergence of a General Inertial Proximal Splitting Scheme
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.
Multi-Manifold Deep Metric Learning for Image Set Classification
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.
Deep Hashing for Compact Binary Codes Learning
In this paper, we propose a new deep hashing (DH) approach to learn compact binary codes for large scale visual search.
Motion Part Regularization: Improving Action Recognition via Trajectory Selection
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.
Multiple Granularity Analysis for Fine-grained Action Detection
We propose to decompose the fine-grained human activity analysis problem into two sequential tasks with increasing granularity.
Beta Process Multiple Kernel Learning
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.
