Ada-VSR: Adaptive Video Super-Resolution with Meta-Learning

no code implementations • 5 Aug 2021 • Akash Gupta, Padmaja Jonnalagedda, Bir Bhanu, Amit K. Roy-Chowdhury

Specifically, meta-learning is employed to obtain adaptive parameters, using a large-scale external dataset, that can adapt quickly to the novel condition (degradation model) of the given test video during the internal learning task, thereby exploiting external and internal information of a video for super-resolution.

Meta-Learning Transfer Learning +1

SAGE: Sequential Attribute Generator for Analyzing Glioblastomas using Limited Dataset

no code implementations • 14 May 2020 • Padmaja Jonnalagedda, Brent Weinberg, Jason Allen, Taejin L. Min, Shiv Bhanu, Bir Bhanu

While deep learning approaches have shown remarkable performance in many imaging tasks, most of these methods rely on availability of large quantities of data.

Attribute

Feature Disentanglement to Aid Imaging Biomarker Characterization for Genetic Mutations

no code implementations • MIDL 2019 • Padmaja Jonnalagedda, Brent Weinberg, Jason Allen, Bir Bhanu

Various mutations have been shown to correlate with prognosis of High-Grade Glioma (Glioblastoma).

Disentanglement Generative Adversarial Network