10 citations · 10 across the 1 of their papers we have counts for
4 papers
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…
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…
Model Inversion Robustness: Can Transfer Learning Help?
Sy-Tuyen Ho, Koh Jun Hao, Keshigeyan Chandrasegaran +2
Model Inversion (MI) attacks aim to reconstruct private training data by abusing access to machine learning models. Contemporary MI attacks have achieved impressive attack performa…