Search Results for author: Puneesh Deora

Found 5 papers, 3 papers with code

GradML: A Gradient-based Loss for Deep Metric Learning

no code implementations NeurIPS Workshop ICBINB 2021 Bhavya Vasudeva, Puneesh Deora, Saumik Bhattacharya, Umapada Pal, Sukalpa Chanda

Deep metric learning (ML) uses a carefully designed loss function to learn distance metrics for improving the discriminatory ability for tasks like clustering and retrieval.

Metric Learning

LoOp: Looking for Optimal Hard Negative Embeddings for Deep Metric Learning

1 code implementation ICCV 2021 Bhavya Vasudeva, Puneesh Deora, Saumik Bhattacharya, Umapada Pal, Sukalpa Chanda

Deep metric learning has been effectively used to learn distance metrics for different visual tasks like image retrieval, clustering, etc.

Image Retrieval Metric Learning

Co-VeGAN: Complex-Valued Generative Adversarial Network for Compressive Sensing MR Image Reconstruction

no code implementations24 Feb 2020 Bhavya Vasudeva, Puneesh Deora, Saumik Bhattacharya, Pyari Mohan Pradhan

Although state-of-the-art deep learning based methods have been able to obtain fast, high-quality reconstruction of CS-MR images, their main drawback is that they treat complex-valued MRI data as real-valued entities.

Compressive Sensing MRI Reconstruction

Structure Preserving Compressive Sensing MRI Reconstruction using Generative Adversarial Networks

1 code implementation14 Oct 2019 Puneesh Deora, Bhavya Vasudeva, Saumik Bhattacharya, Pyari Mohan Pradhan

Compressive sensing magnetic resonance imaging (CS-MRI) accelerates the acquisition of MR images by breaking the Nyquist sampling limit.

Compressive Sensing MRI Reconstruction

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