7 citations · 23 across the 12 of their papers we have counts for
12 papers
Data-to-Model Distillation: Data-Efficient Learning Framework
Ahmad Sajedi, Samir Khaki, Lucy Z. Liu +3
Dataset distillation aims to distill the knowledge of a large-scale real dataset into small yet informative synthetic data such that a model trained on it performs as well as a mod…
Emphasizing Discriminative Features for Dataset Distillation in Complex Scenarios
Kai Wang, Zekai Li, Zhi-Qi Cheng +6
Dataset distillation has demonstrated strong performance on simple datasets like CIFAR, MNIST, and TinyImageNet but struggles to achieve similar results in more complex scenarios.…
GSTAM: Efficient Graph Distillation with Structural Attention-Matching
Arash Rasti-Meymandi, Ahmad Sajedi, Zhaopan Xu +1
Graph distillation has emerged as a solution for reducing large graph datasets to smaller, more manageable, and informative ones. Existing methods primarily target node classificat…
Prioritize Alignment in Dataset Distillation
Zekai Li, Ziyao Guo, Wangbo Zhao +8
Dataset Distillation aims to compress a large dataset into a significantly more compact, synthetic one without compromising the performance of the trained models. To achieve this,…
ATOM: Attention Mixer for Efficient Dataset Distillation
Samir Khaki, Ahmad Sajedi, Kai Wang +3
Recent works in dataset distillation seek to minimize training expenses by generating a condensed synthetic dataset that encapsulates the information present in a larger real datas…
ProbMCL: Simple Probabilistic Contrastive Learning for Multi-label Visual Classification
Ahmad Sajedi, Samir Khaki, Yuri A. Lawryshyn +1
Multi-label image classification presents a challenging task in many domains, including computer vision and medical imaging. Recent advancements have introduced graph-based and tra…
DataDAM: Efficient Dataset Distillation with Attention Matching
Ahmad Sajedi, Samir Khaki, Ehsan Amjadian +3
Researchers have long tried to minimize training costs in deep learning while maintaining strong generalization across diverse datasets. Emerging research on dataset distillation a…
End-to-End Supervised Multilabel Contrastive Learning
Ahmad Sajedi, Samir Khaki, Konstantinos N. Plataniotis +1
Multilabel representation learning is recognized as a challenging problem that can be associated with either label dependencies between object categories or data-related issues suc…
A New Probabilistic Distance Metric With Application In Gaussian Mixture Reduction
Ahmad Sajedi, Yuri A. Lawryshyn, Konstantinos N. Plataniotis
This paper presents a new distance metric to compare two continuous probability density functions. The main advantage of this metric is that, unlike other statistical measurements,…
Subclass Knowledge Distillation with Known Subclass Labels
Ahmad Sajedi, Yuri A. Lawryshyn, Konstantinos N. Plataniotis
This work introduces a novel knowledge distillation framework for classification tasks where information on existing subclasses is available and taken into consideration. In classi…
On the Efficiency of Subclass Knowledge Distillation in Classification Tasks
Ahmad Sajedi, Konstantinos N. Plataniotis
This work introduces a novel knowledge distillation framework for classification tasks where information on existing subclasses is available and taken into consideration. In classi…
High Performance Convolution Using Sparsity and Patterns for Inference in Deep Convolutional Neural Networks
Hossam Amer, Ahmed H. Salamah, Ahmad Sajedi +1
Deploying deep Convolutional Neural Networks (CNNs) is impacted by their memory footprint and speed requirements, which mainly come from convolution. Widely-used convolution algori…