Search Results for author:
Found 9 papers, 3 papers with code
Delving Deep into Simplicity Bias for Long-Tailed Image Recognition
Simplicity Bias (SB) is a phenomenon that deep neural networks tend to rely favorably on simpler predictive patterns but ignore some complex features when applied to supervised discriminative tasks.
Ranked #4 on
Long-tail Learning
on CIFAR-10-LT (ρ=10)
Exploration strategies for articulatory synthesis of complex syllable onsets
High-quality articulatory speech synthesis has many potential applications in speech science and technology.
Lifelong Topological Visual Navigation
Commonly, learning-based topological navigation approaches produce a local policy while preserving some loose connectivity of the space through a topological map.
Evaluating Features and Metrics for High-Quality Simulation of Early Vocal Learning of Vowels
One of the crucial parameters in these simulations is the choice of features and a metric to evaluate the acoustic error between the synthesised sound and the reference target.
Adversarial Examples in Modern Machine Learning: A Review
Recent research has found that many families of machine learning models are vulnerable to adversarial examples: inputs that are specifically designed to cause the target model to produce erroneous outputs.
Physical Adversarial Textures that Fool Visual Object Tracking
We present a system for generating inconspicuous-looking textures that, when displayed in the physical world as digital or printed posters, cause visual object tracking systems to become confused.
Hinted Networks
We present Hinted Networks: a collection of architectural transformations for improving the accuracies of neural network models for regression tasks, through the injection of a prior for the output prediction (i. e. a hint).
Maximal Jacobian-based Saliency Map Attack
The Jacobian-based Saliency Map Attack is a family of adversarial attack methods for fooling classification models, such as deep neural networks for image classification tasks.
Underwater Multi-Robot Convoying using Visual Tracking by Detection
We present a robust multi-robot convoying approach that relies on visual detection of the leading agent, thus enabling target following in unstructured 3-D environments.
