581 citations · 788 across the 10 of their papers we have counts for
11 papers
ZOPGA-KD: Zeroth Order PGA Guided Data Free Knowledge Distillation
Tuhin Subhra Mukherjee, Tiya Rose Pulikunnel, Vaidhav Adit +2
Knowledge distillation (KD) enables the transfer of learned representations from a large teacher network to a small student network. In foundational KD, the student network uses th…
Balancing Uncertainty and Diversity of Samples: Leveraging Diversity of Least, High Confidence Samples for Effective Active Learning
Vipul Arya, S. H. Shabbeer Basha, Srikrishna U N +2
Deep learning models, including Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), have achieved state-of-the-art performance on various computer vision tasks suc…
Target Aware Network Architecture Search and Compression for Efficient Knowledge Transfer
S. H. Shabbeer Basha, Debapriya Tula, Sravan Kumar Vinakota +1
Transfer Learning enables Convolutional Neural Networks (CNN) to acquire knowledge from a source domain and transfer it to a target domain, where collecting large-scale annotated e…
HRel: Filter Pruning based on High Relevance between Activation Maps and Class Labels
CH Sarvani, Mrinmoy Ghorai, Shiv Ram Dubey +1
This paper proposes an Information Bottleneck theory based filter pruning method that uses a statistical measure called Mutual Information (MI). The MI between filters and class la…
AdaInject: Injection Based Adaptive Gradient Descent Optimizers for Convolutional Neural Networks
Shiv Ram Dubey, S. H. Shabbeer Basha, Satish Kumar Singh +1
The convolutional neural networks (CNNs) are generally trained using stochastic gradient descent (SGD) based optimization techniques. The existing SGD optimizers generally suffer w…
Deep Model Compression based on the Training History
S. H. Shabbeer Basha, Mohammad Farazuddin, Viswanath Pulabaigari +2
Deep Convolutional Neural Networks (DCNNs) have shown promising performances in several visual recognition problems which motivated the researchers to propose popular architectures…
AutoTune: Automatically Tuning Convolutional Neural Networks for Improved Transfer Learning
S. H. Shabbeer Basha, Sravan Kumar Vinakota, Viswanath Pulabaigari +2
Transfer learning enables solving a specific task having limited data by using the pre-trained deep networks trained on large-scale datasets. Typically, while transferring the lear…
An Information-rich Sampling Technique over Spatio-Temporal CNN for Classification of Human Actions in Videos
S. H. Shabbeer Basha, Viswanath Pulabaigari, Snehasis Mukherjee
We propose a novel scheme for human action recognition in videos, using a 3-dimensional Convolutional Neural Network (3D CNN) based classifier. Traditionally in deep learning based…
AutoFCL: Automatically Tuning Fully Connected Layers for Handling Small Dataset
S. H. Shabbeer Basha, Sravan Kumar Vinakota, Shiv Ram Dubey +2
Deep Convolutional Neural Networks (CNN) have evolved as popular machine learning models for image classification during the past few years, due to their ability to learn the probl…
Impact of Fully Connected Layers on Performance of Convolutional Neural Networks for Image Classification
S. H. Shabbeer Basha, Shiv Ram Dubey, Viswanath Pulabaigari +1
The Convolutional Neural Networks (CNNs), in domains like computer vision, mostly reduced the need for handcrafted features due to its ability to learn the problem-specific feature…
RCCNet: An Efficient Convolutional Neural Network for Histological Routine Colon Cancer Nuclei Classification
S H Shabbeer Basha, Soumen Ghosh, Kancharagunta Kishan Babu +3
Efficient and precise classification of histological cell nuclei is of utmost importance due to its potential applications in the field of medical image analysis. It would facilita…