16 citations · 37 across the 15 of their papers we have counts for
17 papers
PSF-4D: A Progressive Sampling Framework for View Consistent 4D Editing
Hasan Iqbal, Nazmul Karim, Umar Khalid +4
Instruction-guided generative models, especially those using text-to-image (T2I) and text-to-video (T2V) diffusion frameworks, have advanced the field of content editing in recent…
Fisher Information guided Purification against Backdoor Attacks
Nazmul Karim, Abdullah Al Arafat, Adnan Siraj Rakin +2
Studies on backdoor attacks in recent years suggest that an adversary can compromise the integrity of a deep neural network (DNN) by manipulating a small set of training samples. O…
Augmented Neural Fine-Tuning for Efficient Backdoor Purification
Nazmul Karim, Abdullah Al Arafat, Umar Khalid +2
Recent studies have revealed the vulnerability of deep neural networks (DNNs) to various backdoor attacks, where the behavior of DNNs can be compromised by utilizing certain types…
Free-Editor: Zero-shot Text-driven 3D Scene Editing
Nazmul Karim, Hasan Iqbal, Umar Khalid +2
Text-to-Image (T2I) diffusion models have recently gained traction for their versatility and user-friendliness in 2D content generation and editing. However, training a diffusion m…
LatentEditor: Text Driven Local Editing of 3D Scenes
Umar Khalid, Hasan Iqbal, Nazmul Karim +2
While neural fields have made significant strides in view synthesis and scene reconstruction, editing them poses a formidable challenge due to their implicit encoding of geometry a…
Efficient Backdoor Removal Through Natural Gradient Fine-tuning
Nazmul Karim, Abdullah Al Arafat, Umar Khalid +2
The success of a deep neural network (DNN) heavily relies on the details of the training scheme; e.g., training data, architectures, hyper-parameters, etc. Recent backdoor attacks…
SAVE: Spectral-Shift-Aware Adaptation of Image Diffusion Models for Text-driven Video Editing
Nazmul Karim, Umar Khalid, Mohsen Joneidi +2
Text-to-Image (T2I) diffusion models have achieved remarkable success in synthesizing high-quality images conditioned on text prompts. Recent methods have tried to replicate the su…
C-SFDA: A Curriculum Learning Aided Self-Training Framework for Efficient Source Free Domain Adaptation
Nazmul Karim, Niluthpol Chowdhury Mithun, Abhinav Rajvanshi +3
Unsupervised domain adaptation (UDA) approaches focus on adapting models trained on a labeled source domain to an unlabeled target domain. UDA methods have a strong assumption that…
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…
RODD: A Self-Supervised Approach for Robust Out-of-Distribution Detection
Umar Khalid, Ashkan Esmaeili, Nazmul Karim +1
Recent studies have addressed the concern of detecting and rejecting the out-of-distribution (OOD) samples as a major challenge in the safe deployment of deep learning (DL) models.…
The Prominence of Artificial Intelligence in COVID-19
MD Abdullah Al Nasim, Aditi Dhali, Faria Afrin +3
In December 2019, a novel virus called COVID-19 had caused an enormous number of causalities to date. The battle with the novel Coronavirus is baffling and horrifying after the Spa…
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…
Odyssey: Creation, Analysis and Detection of Trojan Models
Marzieh Edraki, Nazmul Karim, Nazanin Rahnavard +2
Along with the success of deep neural network (DNN) models, rise the threats to the integrity of these models. A recent threat is the Trojan attack where an attacker interferes wit…