5 papers
InstructMixup: Instruction-Guided Salient Patch Editing for Robust Data Augmentation
Khawar Islam, Arif Mahmood, Xin Jin +1
In image and video technologies, data augmentation is widely used to improve the generalization of deep visual models, and mixup-based strategies that interpolate between samples h…
-FracMix: Label-Preserving Self-Saliency Mixup Augmentation
Khawar Islam, Arif Mahmood, Xin Jin +1
Data augmentation is known to improve generalization of deep visual models. Recent methods favor mixup strategies that generate interpolated samples to improve model performance. H…
HSFM: Hard-Set-Guided Feature-Space Meta-Learning for Robust Classification under Spurious Correlations
Aryan Yazdan Parast, Khawar Islam, Soyoun Won +2
Deep neural networks often rely on spurious features to make predictions, which makes them brittle under distribution shift and on samples where the spurious correlation does not h…
GenMix: Effective Data Augmentation with Generative Diffusion Model Image Editing
Khawar Islam, Muhammad Zaigham Zaheer, Arif Mahmood +2
Data augmentation is widely used to enhance generalization in visual classification tasks. However, traditional methods struggle when source and target domains differ, as in domain…
Context-guided Responsible Data Augmentation with Diffusion Models
Khawar Islam, Naveed Akhtar
Generative diffusion models offer a natural choice for data augmentation when training complex vision models. However, ensuring reliability of their generative content as augmentat…