18 citations · 37 across the 10 of their papers we have counts for
5 papers · 1 filter
Exploring Compositionality in Vision Transformers using Wavelet Representations
Akshad Shyam Purushottamdas, Pranav K Nayak, Divya Mehul Rajparia +4
While insights into the workings of the transformer model have largely emerged by analysing their behaviour on language tasks, this work investigates the representations learnt by…
Inpainting the Gaps: A Novel Framework for Evaluating Explanation Methods in Vision Transformers
Lokesh Badisa, Sumohana S. Channappayya
The perturbation test remains the go-to evaluation approach for explanation methods in computer vision. This evaluation method has a major drawback of test-time distribution shift…
Minimizing Energy Costs in Deep Learning Model Training: The Gaussian Sampling Approach
Challapalli Phanindra Revanth, Sumohana S. Channappayya, C Krishna Mohan
Computing the loss gradient via backpropagation consumes considerable energy during deep learning (DL) model training. In this paper, we propose a novel approach to efficiently com…
Quality Aware Generative Adversarial Networks
Parimala Kancharla, Sumohana S. Channappayya
Generative Adversarial Networks (GANs) have become a very popular tool for implicitly learning high-dimensional probability distributions. Several improvements have been made to th…
No Reference Stereoscopic Video Quality Assessment Using Joint Motion and Depth Statistics
Appina Balasubramanyam, Jalli Akshith, Battula Shanmukh Srinivas +1
We present a no reference (NR) quality assessment algorithm for assessing the perceptual quality of natural stereoscopic 3D (S3D) videos. This work is inspired by our finding that…