182 citations · 349 across the 12 of their papers we have counts for
4 papers · 1 filter
On Lipschitz Bounds of General Convolutional Neural Networks
Dongmian Zou, Radu Balan, Maneesh Singh
Many convolutional neural networks (CNNs) have a feed-forward structure. In this paper, a linear program that estimates the Lipschitz bound of such CNNs is proposed. Several CNNs,…
Disconnected Manifold Learning for Generative Adversarial Networks
Mahyar Khayatkhoei, Ahmed Elgammal, Maneesh Singh
Natural images may lie on a union of disjoint manifolds rather than one globally connected manifold, and this can cause several difficulties for the training of common Generative A…
Adversarial Learning of Raw Speech Features for Domain Invariant Speech Recognition
Aditay Tripathi, Aanchan Mohan, Saket Anand +1
Recent advances in neural network based acoustic modelling have shown significant improvements in automatic speech recognition (ASR) performance. In order for acoustic models to be…
Disentangling Factors of Variation with Cycle-Consistent Variational Auto-Encoders
Ananya Harsh Jha, Saket Anand, Maneesh Singh +1
Generative models that learn disentangled representations for different factors of variation in an image can be very useful for targeted data augmentation. By sampling from the dis…