31 papers
Explanation Stability of Test-Time Adaptation in Computational Pathology: A Large-Scale Benchmark
R. G. Bahumanya, Harshith V. M., Shreyank N. Gowda +1
Test-time adaptation (TTA) has become a practical way to adapt deployed models to unlabeled target data, a setting that is especially relevant in computational pathology where stai…
From Pixels to Portraits: A Comprehensive Survey of Talking Head Generation Techniques and Applications
Shreyank N Gowda, Dheeraj Pandey, Shashank Narayana Gowda
Talking head generation has progressed rapidly from landmark- and GAN-based facial animation to diffusion models, neural rendering, 3D-aware avatars, and foundation-model-assisted…
Erasing Without Collateral Damage: Precise Concept Removal in Diffusion Models
Parth Upman, Nishita Jain, Shreyank N Gowda
Training-free concept erasure is an attractive mechanism for controlling text-to-image diffusion models, but precise erasure often comes at the cost of damaging semantically relate…
QC-SMOTE: Quality-Controlled SMOTE for Imbalanced Classification
Parth Upman, Shreyank N Gowda
Class imbalance poses a significant challenge in classification, where existing methods such as SMOTE often generate low-quality synthetic samples in regions with noise or class ov…
Uncertainty-Aware Longitudinal Forecasting of Alzheimer's Disease Progression Using Deep Learning
Arya Hariharan, Shreyank N Gowda, Anala M R
Longitudinal modelling of Alzheimer's disease progression is clinically useful only if it can describe not just the most likely next diagnosis, but how a patient may evolve over ti…
Using Explainability as a Training-Time Reliability Signal for Efficient ECG Classification
Veerendhra Kumar Dangeti, Xiao Gu, Ying Weng +1
Training deep neural networks for clinical time-series analysis is computationally demanding, yet many healthcare settings lack the resources required for repeated model developmen…