24 citations · 99 across the 33 of their papers we have counts for
12 papers · 1 filter
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…
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.…
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…
LSDAT: Low-Rank and Sparse Decomposition for Decision-based Adversarial Attack
Ashkan Esmaeili, Marzieh Edraki, Nazanin Rahnavard +2
We propose LSDAT, an image-agnostic decision-based black-box attack that exploits low-rank and sparse decomposition (LSD) to dramatically reduce the number of queries and achieve s…
DyLoc: Dynamic Localization for Massive MIMO Using Predictive Recurrent Neural Networks
Farzam Hejazi, Katarina Vuckovic, Nazanin Rahnavard
This paper presents a data-driven localization framework with high precision in time-varying complex multipath environments, such as dense urban areas and indoors, where GPS and mo…
Cassandra: Detecting Trojaned Networks from Adversarial Perturbations
Xiaoyu Zhang, Ajmal Mian, Rohit Gupta +2
Deep neural networks are being widely deployed for many critical tasks due to their high classification accuracy. In many cases, pre-trained models are sourced from vendors who may…
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…
Iterative Projection and Matching: Finding Structure-preserving Representatives and Its Application to Computer Vision
Mohsen Joneidi, Alireza Zaeemzadeh, Nazanin Rahnavard +1
The goal of data selection is to capture the most structural information from a set of data. This paper presents a fast and accurate data selection method, in which the selected sa…
Norm-Preservation: Why Residual Networks Can Become Extremely Deep?
Alireza Zaeemzadeh, Nazanin Rahnavard, Mubarak Shah
Augmenting neural networks with skip connections, as introduced in the so-called ResNet architecture, surprised the community by enabling the training of networks of more than 1,00…