Spectral Graph Pruning Against Over-Squashing and Over-Smoothing

no code implementations • 6 Apr 2024 • Adarsh Jamadandi, Celia Rubio-Madrigal, Rebekka Burkholz

Message Passing Graph Neural Networks are known to suffer from two problems that are sometimes believed to be diametrically opposed: over-squashing and over-smoothing.

Augmented Data as an Auxiliary Plug-in Towards Categorization of Crowdsourced Heritage Data

no code implementations • 8 Jul 2021 • Shashidhar Veerappa Kudari, Akshaykumar Gunari, Adarsh Jamadandi, Ramesh Ashok Tabib, Uma Mudenagudi

In this paper, we propose a strategy to mitigate the problem of inefficient clustering performance by introducing data augmentation as an auxiliary plug-in.

Clustering Data Augmentation +1

Deep Visual Attention-Based Transfer Clustering

no code implementations • 6 Jul 2021 • Akshaykumar Gunari, Shashidhar Veerappa Kudari, Sukanya Nadagadalli, Keerthi Goudnaik, Ramesh Ashok Tabib, Uma Mudenagudi, Adarsh Jamadandi

In this paper, we propose a methodology to improvise the technique of deep transfer clustering (DTC) when applied to the less variant data distribution.

Clustering Image Clustering

Exemplar based underwater image enhancement augmented by Wavelet Corrected Transforms

1 code implementation • The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 11-17 2019 • Adarsh Jamadandi, Uma Mudenagudi

In this paper we propose a novel deep learning framework to enhance underwater images by augmenting our network with wavelet corrected transformations.

Image Enhancement SSIM +1