1 citations · 1 across the 6 of their papers we have counts for
6 papers
Erasing More Than Intended? How Concept Erasure Degrades the Generation of Non-Target Concepts
Ibtihel Amara, Ahmed Imtiaz Humayun, Ivana Kajic +12
Concept erasure techniques have recently gained significant attention for their potential to remove unwanted concepts from text-to-image models. While these methods often demonstra…
What Secrets Do Your Manifolds Hold? Understanding the Local Geometry of Generative Models
Ahmed Imtiaz Humayun, Ibtihel Amara, Cristina Vasconcelos +7
Deep Generative Models are frequently used to learn continuous representations of complex data distributions using a finite number of samples. For any generative model, including p…
Dynamic Corrective Self-Distillation for Better Fine-Tuning of Pretrained Models
Ibtihel Amara, Vinija Jain, Aman Chadha
We tackle the challenging issue of aggressive fine-tuning encountered during the process of transfer learning of pre-trained language models (PLMs) with limited labeled downstream…
Improved Contextual Recognition In Automatic Speech Recognition Systems By Semantic Lattice Rescoring
Ankitha Sudarshan, Vinay Samuel, Parth Patwa +2
Automatic Speech Recognition (ASR) has witnessed a profound research interest. Recent breakthroughs have given ASR systems different prospects such as faithfully transcribing spoke…
BD-KD: Balancing the Divergences for Online Knowledge Distillation
Ibtihel Amara, Nazanin Sepahvand, Brett H. Meyer +2
We address the challenge of producing trustworthy and accurate compact models for edge devices. While Knowledge Distillation (KD) has improved model compression in terms of achievi…
CES-KD: Curriculum-based Expert Selection for Guided Knowledge Distillation
Ibtihel Amara, Maryam Ziaeefard, Brett H. Meyer +2
Knowledge distillation (KD) is an effective tool for compressing deep classification models for edge devices. However, the performance of KD is affected by the large capacity gap b…