no code implementations • ICLR 2019 • Hariharan Ravishankar, Rahul Venkataramani, Saihareesh Anamandra, Prasad Sudhakar
Despite the recent advances in representation learning, lifelong learning continues to be one of the most challenging and unconquered problems.
no code implementations • 26 Oct 2023 • Rachana Sathish, Rahul Venkataramani, K S Shriram, Prasad Sudhakar
In this work, we propose a plug-and-play Prompt Optimization Technique for foundation models like SAM (SAMPOT) that utilizes the downstream segmentation task to optimize the human-provided prompt to obtain improved performance.
no code implementations • 4 Dec 2018 • Rahul Venkataramani, Hariharan Ravishankar, Saihareesh Anamandra
Deep learning algorithms have demonstrated tremendous success on challenging medical imaging problems.
no code implementations • 20 Apr 2017 • Hariharan Ravishankar, Prasad Sudhakar, Rahul Venkataramani, Sheshadri Thiruvenkadam, Pavan Annangi, Narayanan Babu, Vivek Vaidya
In this paper, we systematically investigate the process of transferring a Convolutional Neural Network, trained on ImageNet images to perform image classification, to kidney detection problem in ultrasound images.
no code implementations • 8 Dec 2016 • Rahul Venkataramani, Sheshadri Thiruvenkadam, Prasad Sudhakar, Hariharan Ravishankar, Vivek Vaidya
Typical convolutional neural networks (CNNs) have several millions of parameters and require a large amount of annotated data to train them.