5 citations · 12 across the 6 of their papers we have counts for
10 papers
Deep is a Luxury We Don't Have
Ahmed Taha, Yen Nhi Truong Vu, Brent Mombourquette +3
Medical images come in high resolutions. A high resolution is vital for finding malignant tissues at an early stage. Yet, this resolution presents a challenge in terms of modeling…
Knowledge Evolution in Neural Networks
Ahmed Taha, Abhinav Shrivastava, Larry Davis
Deep learning relies on the availability of a large corpus of data (labeled or unlabeled). Thus, one challenging unsettled question is: how to train a deep network on a relatively…
SVMax: A Feature Embedding Regularizer
Ahmed Taha, Alex Hanson, Abhinav Shrivastava +1
A neural network regularizer (e.g., weight decay) boosts performance by explicitly penalizing the complexity of a network. In this paper, we penalize inferior network activations -…
A Generic Visualization Approach for Convolutional Neural Networks
Ahmed Taha, Xitong Yang, Abhinav Shrivastava +1
Retrieval networks are essential for searching and indexing. Compared to classification networks, attention visualization for retrieval networks is hardly studied. We formulate att…
Unsupervised Data Uncertainty Learning in Visual Retrieval Systems
Ahmed Taha, Yi-Ting Chen, Teruhisa Misu +2
We introduce an unsupervised formulation to estimate heteroscedastic uncertainty in retrieval systems. We propose an extension to triplet loss that models data uncertainty for each…
Exploring Uncertainty in Conditional Multi-Modal Retrieval Systems
Ahmed Taha, Yi-Ting Chen, Xitong Yang +2
We cast visual retrieval as a regression problem by posing triplet loss as a regression loss. This enables epistemic uncertainty estimation using dropout as a Bayesian approximatio…