1 code implementation • 7 May 2024 • Kumar Shubham, Aishwarya Jayagopal, Syed Mohammed Danish, Prathosh AP, Vaibhav Rajan
Cancer, a leading cause of death globally, occurs due to genomic changes and manifests heterogeneously across patients.
no code implementations • 1 Mar 2024 • Goirik Chakrabarty, Aditya Chandrasekar, Ramya Hebbalaguppe, Prathosh AP
Our experiments against existing state-of-the-art methods demonstrate the improved effectiveness of our approach in terms of both image editing quality and inference speed.
1 code implementation • 22 Dec 2023 • Subhodip Panda, Prathosh AP
The heightened emphasis on the regulation of deep generative models, propelled by escalating concerns pertaining to privacy and compliance with regulatory frameworks, underscores the imperative need for precise control mechanisms over these models.
1 code implementation • 16 Dec 2023 • Kumar Shubham, Pranav Sastry, Prathosh AP
In our work, we address these challenges by (i) implementing a noise-aware classifier using the pseudo labels generated by the label model (ii) utilizing the noise-aware classifier's prediction to train the label model and generate class-conditional images.
1 code implementation • CVPR 2023 • Aayush Kumar Tyagi, Chirag Mohapatra, Prasenjit Das, Govind Makharia, Lalita Mehra, Prathosh AP, Mausam
While there exist multiple, general-purpose, deep learning-based object detection and counting methods, they may not readily transfer to detecting and counting cells in medical images, due to the limited data, presence of tiny overlapping objects, multiple cell types, severe class-imbalance, minute differences in size/shape of cells, etc.
1 code implementation • 17 Mar 2023 • Utkarsh Pratiush, Arshed Nabeel, Vishwesha Guttal, Prathosh AP
Collective motion is an ubiquitous phenomenon in nature, inspiring engineers, physicists and mathematicians to develop mathematical models and bio-inspired designs.
1 code implementation • 7 Feb 2022 • Durga Sivasubramanian, Ayush Maheshwari, Pradeep Shenoy, Prathosh AP, Ganesh Ramakrishnan
In several supervised learning scenarios, auxiliary losses are used in order to introduce additional information or constraints into the supervised learning objective.
1 code implementation • 16 Jul 2021 • Arnab Kumar Mondal, Himanshu Asnani, Parag Singla, Prathosh AP
The basic idea in RAEs is to learn a non-linear mapping from the high-dimensional data space to a low-dimensional latent space and vice-versa, simultaneously imposing a distributional prior on the latent space, which brings in a regularization effect.
no code implementations • CVPR 2021 • Prashant Pandey, Mrigank Raman, Sumanth Varambally, Prathosh AP
Generalization of machine learning models trained on a set of source domains on unseen target domains with different statistics, is a challenging problem.
no code implementations • 12 Jun 2021 • Prashant Pandey, Ajey Pai, Nisarg Bhatt, Prasenjit Das, Govind Makharia, Prathosh AP, Mausam
We evaluate our method on four public medical segmentation datasets and a novel histopathology dataset that we introduce.
no code implementations • 1 Mar 2021 • Prashant Pandey, Mrigank Raman, Sumanth Varambally, Prathosh AP
Generalization of machine learning models trained on a set of source domains on unseen target domains with different statistics, is a challenging problem.
no code implementations • 3 Oct 2020 • Ayush Srivastava, Oshin Dutta, Prathosh AP, Sumeet Agarwal, Jigyasa Gupta
In the last few years, compression of deep neural networks has become an important strand of machine learning and computer vision research.
no code implementations • 28 Jul 2020 • Prashant Pandey, Mrigank Raman, Sumanth Varambally, Prathosh AP
Features extracted from this source domain are learned using a generative model whose latent space is used as a sampler to retrieve the nearest neighbors for the target data points.
2 code implementations • ECCV 2020 • Prashant Pandey, Aayush Kumar Tyagi, Sameer Ambekar, Prathosh AP
Therefore we cast the skin segmentation problem as that of target-independent Unsupervised Domain Adaptation (UDA) where we use the data from the Red-channel of the visible-range to develop skin segmentation algorithm on NIR images.
no code implementations • 10 Jun 2020 • Arnab Kumar Mondal, Himanshu Asnani, Parag Singla, Prathosh AP
Specifically, we consider the class of RAEs with deterministic Encoder-Decoder pairs, Wasserstein Auto-Encoders (WAE), and show that having a fixed prior distribution, \textit{a priori}, oblivious to the dimensionality of the `true' latent space, will lead to the infeasibility of the optimization problem considered.
no code implementations • 17 May 2020 • Arnab Kumar Mondal, Arnab Bhattacharya, Sudipto Mukherjee, Prathosh AP, Sreeram Kannan, Himanshu Asnani
Estimation of information theoretic quantities such as mutual information and its conditional variant has drawn interest in recent times owing to their multifaceted applications.
1 code implementation • 11 May 2020 • Prashant Pandey, Prathosh AP, Vinay Kyatham, Deepak Mishra, Tathagato Rai Dastidar
We prove the existence of such a clone given that infinite number of data points can be sampled from the source distribution.
no code implementations • 10 Dec 2019 • Arnab Kumar Mondal, Sankalan Pal Chowdhury, Aravind Jayendran, Parag Singla, Himanshu Asnani, Prathosh AP
The field of neural generative models is dominated by the highly successful Generative Adversarial Networks (GANs) despite their challenges, such as training instability and mode collapse.
1 code implementation • 11 Nov 2019 • Prashant Pandey, Prathosh AP, Manu Kohli, Josh Pritchard
In this paper, we propose to automate the response measurement through video recording of the scene following the use of Deep Neural models for human action recognition from videos.
1 code implementation • 24 Mar 2019 • Vinay Kyatham, Mayank Mishra, Tarun Kumar Yadav, Deepak Mishra, Prathosh AP
Specifically, we simultaneously auto-encode the data manifold and its perturbations implicitly through the perturbations of the regularized and quantized generative latent space, realized using variational inference.
no code implementations • 27 Sep 2018 • Deepak Mishra, Prathosh AP, Aravind J, Prashant Pandey, Santanu Chaudhury
Conditional generation refers to the process of sampling from an unknown distribution conditioned on semantics of the data.