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cs.LG2026
When One Adapter Speaks for Many: Discovering Low-Rank Redundancy in Continual Fine-Tuning
Tanguy Dieudonné, Giulia Lanzillotta, Enis Simsar +2
Low-Rank Adaptation (LoRA) has become the standard tool for parameter-efficient fine-tuning of large pretrained models. When applied sequentially across tasks in Continual Learning…
cs.LG2026
SeqLoRA: Bilevel Orthogonal Adaptation for Continual Multi-Concept Generation
Javad Parsa, Enis Simsar, Amir Joudaki +2
Parameter-efficient fine-tuning enables fast personalization of text-to-image diffusion models, but composing multiple custom concepts remains challenging due to representation int…
cs.LG2021
LatentCLR: A Contrastive Learning Approach for Unsupervised Discovery of Interpretable Directions
Oğuz Kaan Yüksel, Enis Simsar, Ezgi Gülperi Er +1
Recent research has shown that it is possible to find interpretable directions in the latent spaces of pre-trained Generative Adversarial Networks (GANs). These directions enable c…