40 citations · 96 across the 15 of their papers we have counts for
17 papers
Synthesis Without Training: An Inference-Only Pipeline for Tabular, Temporal, and Relational Synthetic Data
Zilong Zhao, Abdul Raheem, Jiayu Li +5
Synthetic data generation is dominated by the fit-then-sample paradigm: a generative model is trained on a private dataset and then sampled from. Despite its widespread adoption, t…
Instruction Tuning of Large Language Models for Tabular Data Generation-in One Day
Milad Abdollahzadeh, Abdul Raheem, Zilong Zhao +5
Tabular instruction tuning has emerged as a promising research direction for improving LLMs understanding of tabular data. However, the majority of existing works only consider que…
AIR: Zero-shot Generative Model Adaptation with Iterative Refinement
Guimeng Liu, Milad Abdollahzadeh, Ngai-Man Cheung
Zero-shot generative model adaptation (ZSGM) aims to adapt a pre-trained generator to a target domain using only text guidance and without any samples from the target domain. Centr…
FairQueue: Rethinking Prompt Learning for Fair Text-to-Image Generation
Christopher T. H Teo, Milad Abdollahzadeh, Xinda Ma +1
Recently, prompt learning has emerged as the state-of-the-art (SOTA) for fair text-to-image (T2I) generation. Specifically, this approach leverages readily available reference imag…
IRG: Modular Synthetic Relational Database Generation with Complex Relational Schemas
Jiayu Li, Zilong Zhao, Milad Abdollahzadeh +2
Relational databases (RDBs) are widely used by corporations and governments to store multiple related tables. Their relational schemas pose unique challenges to synthetic data gene…
Label-Only Model Inversion Attacks via Knowledge Transfer
Ngoc-Bao Nguyen, Keshigeyan Chandrasegaran, Milad Abdollahzadeh +1
In a model inversion (MI) attack, an adversary abuses access to a machine learning (ML) model to infer and reconstruct private training data. Remarkable progress has been made in t…
On Measuring Fairness in Generative Models
Christopher T. H. Teo, Milad Abdollahzadeh, Ngai-Man Cheung
Recently, there has been increased interest in fair generative models. In this work, we conduct, for the first time, an in-depth study on fairness measurement, a critical component…
A Survey on Generative Modeling with Limited Data, Few Shots, and Zero Shot
Milad Abdollahzadeh, Guimeng Liu, Touba Malekzadeh +3
Generative modeling in machine learning aims to synthesize new data samples that are statistically similar to those observed during training. While conventional generative models s…
Exploring Incompatible Knowledge Transfer in Few-shot Image Generation
Yunqing Zhao, Chao Du, Milad Abdollahzadeh +4
Few-shot image generation (FSIG) learns to generate diverse and high-fidelity images from a target domain using a few (e.g., 10) reference samples. Existing FSIG methods select, pr…
Re-thinking Model Inversion Attacks Against Deep Neural Networks
Ngoc-Bao Nguyen, Keshigeyan Chandrasegaran, Milad Abdollahzadeh +1
Model inversion (MI) attacks aim to infer and reconstruct private training data by abusing access to a model. MI attacks have raised concerns about the leaking of sensitive informa…
Fair Generative Models via Transfer Learning
Christopher TH Teo, Milad Abdollahzadeh, Ngai-Man Cheung
This work addresses fair generative models. Dataset biases have been a major cause of unfairness in deep generative models. Previous work had proposed to augment large, biased data…
Few-shot Image Generation via Adaptation-Aware Kernel Modulation
Yunqing Zhao, Keshigeyan Chandrasegaran, Milad Abdollahzadeh +1
Few-shot image generation (FSIG) aims to learn to generate new and diverse samples given an extremely limited number of samples from a domain, e.g., 10 training samples. Recent wor…
Revisit Multimodal Meta-Learning through the Lens of Multi-Task Learning
Milad Abdollahzadeh, Touba Malekzadeh, Ngai-Man Cheung
Multimodal meta-learning is a recent problem that extends conventional few-shot meta-learning by generalizing its setup to diverse multimodal task distributions. This setup makes a…
Multi-focus Image Fusion for Visual Sensor Networks
Milad Abdollahzadeh, Touba Malekzadeh, Hadi Seyedarabi
Image fusion in visual sensor networks (VSNs) aims to combine information from multiple images of the same scene in order to transform a single image with more information. Image f…
Deep Artifact-Free Residual Network for Single Image Super-Resolution
Hamdollah Nasrollahi, Kamran Farajzadeh, Vahid Hosseini +2
Recently, convolutional neural networks have shown promising performance for single-image super-resolution. In this paper, we propose Deep Artifact-Free Residual (DAFR) network whi…
Fine-grained wound tissue analysis using deep neural network
Hossein Nejati, Hamed Alizadeh Ghazijahani, Milad Abdollahzadeh +4
Tissue assessment for chronic wounds is the basis of wound grading and selection of treatment approaches. While several image processing approaches have been proposed for automatic…
Aircraft Fuselage Defect Detection using Deep Neural Networks
Touba Malekzadeh, Milad Abdollahzadeh, Hossein Nejati +1
To ensure flight safety of aircraft structures, it is necessary to have regular maintenance using visual and nondestructive inspection (NDI) methods. In this paper, we propose an a…