most citedImproved Neural Text Attribute Transfer with Non-parallel Data

10 citations · 15 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2019

Signed Input Regularization

Saeid Asgari Taghanaki, Kumar Abhishek, Ghassan Hamarneh

Over-parameterized deep models usually over-fit to a given training distribution, which makes them sensitive to small changes and out-of-distribution samples at inference time, lea…

math.PR20192 cited

Introduction to Concentration Inequalities

Kumar Abhishek, Sneha Maheshwari, Sujit Gujar

In this report, we aim to exemplify concentration inequalities and provide easy to understand proofs for it. Our focus is on the inequalities which are helpful in the design and an…

cs.CV20192 cited

A Kernelized Manifold Mapping to Diminish the Effect of Adversarial Perturbations

Saeid Asgari Taghanaki, Kumar Abhishek, Shekoofeh Azizi +1

The linear and non-flexible nature of deep convolutional models makes them vulnerable to carefully crafted adversarial perturbations. To tackle this problem, we propose a non-linea…

cs.CV20191 cited

Summarization and Visualization of Large Volumes of Broadcast Video Data

Kumar Abhishek, Ashok Yogi

Over the past few years, there has been an astounding growth in the number of news channels as well as the amount of broadcast news video data. As a result, it is imperative that a…

cs.CL201710 cited

Improved Neural Text Attribute Transfer with Non-parallel Data

Igor Melnyk, Cicero Nogueira dos Santos, Kahini Wadhawan +2

Text attribute transfer using non-parallel data requires methods that can perform disentanglement of content and linguistic attributes. In this work, we propose multiple improvemen…