16 citations · 32 across the 6 of their papers we have counts for
7 papers
UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learning
Nazmul Karim, Mamshad Nayeem Rizve, Nazanin Rahnavard +2
Supervised deep learning methods require a large repository of annotated data; hence, label noise is inevitable. Training with such noisy data negatively impacts the generalization…
CNLL: A Semi-supervised Approach For Continual Noisy Label Learning
Nazmul Karim, Umar Khalid, Ashkan Esmaeili +1
The task of continual learning requires careful design of algorithms that can tackle catastrophic forgetting. However, the noisy label, which is inevitable in a real-world scenario…
RF Signal Transformation and Classification using Deep Neural Networks
Umar Khalid, Nazmul Karim, Nazanin Rahnavard
Deep neural networks (DNNs) designed for computer vision and natural language processing tasks cannot be directly applied to the radio frequency (RF) datasets. To address this chal…
Adversarial Training for Face Recognition Systems using Contrastive Adversarial Learning and Triplet Loss Fine-tuning
Nazmul Karim, Umar Khalid, Nick Meeker +1
Though much work has been done in the domain of improving the adversarial robustness of facial recognition systems, a surprisingly small percentage of it has focused on self-superv…
SPI-GAN: Towards Single-Pixel Imaging through Generative Adversarial Network
Nazmul Karim, Nazanin Rahnavard
Single-pixel imaging is a novel imaging scheme that has gained popularity due to its huge computational gain and potential for a low-cost alternative to imaging beyond the visible…
RL-NCS: Reinforcement learning based data-driven approach for nonuniform compressed sensing
Nazmul Karim, Alireza Zaeemzadeh, Nazanin Rahnavard
A reinforcement-learning-based non-uniform compressed sensing (NCS) framework for time-varying signals is introduced. The proposed scheme, referred to as RL-NCS, aims to boost the…