2 citations · 3 across the 7 of their papers we have counts for
8 papers
Adapting Classifiers To Changing Class Priors During Deployment
Natnael Daba, Bruce McIntosh, Abhijit Mahalanobis
Conventional classifiers are trained and evaluated using balanced data sets in which all classes are equally present. Classifiers are now trained on large data sets such as ImageNe…
Improving Replay Sample Selection and Storage for Less Forgetting in Continual Learning
Daniel Brignac, Niels Lobo, Abhijit Mahalanobis
Continual learning seeks to enable deep learners to train on a series of tasks of unknown length without suffering from the catastrophic forgetting of previous tasks. One effective…
Background Invariant Classification on Infrared Imagery by Data Efficient Training and Reducing Bias in CNNs
Maliha Arif, Calvin Yong, Abhijit Mahalanobis
Even though convolutional neural networks can classify objects in images very accurately, it is well known that the attention of the network may not always be on the semantically i…
Compressing Deep CNNs using Basis Representation and Spectral Fine-tuning
Muhammad Tayyab, Fahad Ahmad Khan, Abhijit Mahalanobis
We propose an efficient and straightforward method for compressing deep convolutional neural networks (CNNs) that uses basis filters to represent the convolutional layers, and opti…
Multiple View Generation and Classification of Mid-wave Infrared Images using Deep Learning
Maliha Arif, Abhijit Mahalanobis
We propose a novel study of generating unseen arbitrary viewpoints for infrared imagery in the non-linear feature subspace . Current methods use synthetic images and often result i…
Attention Guided Anomaly Localization in Images
Shashanka Venkataramanan, Kuan-Chuan Peng, Rajat Vikram Singh +1
Anomaly localization is an important problem in computer vision which involves localizing anomalous regions within images with applications in industrial inspection, surveillance,…