5 accepted papers
Open-set semi-supervised learning (OSSL) leverages unlabeled data containing both in-distribution (ID) and unknown out-of-distribution (OOD) samples, aiming simultaneously to improve closed-set accuracy and detect novel OOD instances. Existing methods either discard valuable information from uncerta
In recent years, machine learning (ML) models have been integrated into various applications and products to improve user experience. However, this approach raises significant concerns about the protection of private user data utilized for training the models. One limitation of vanilla privacy metho…
Scaling architectures have been proven effective for improving Scene Text Recognition (STR), but the individual contribution of vision encoder and text decoder scaling remain under-explored. In this work, we present an in-depth empirical analysis and demonstrate that, contrary to previous observatio…
Parameter-efficient fine-tuning (PEFT) is utilized to adapt large pre-trained machine learning (ML) models to new tasks using a small number of trainable parameters. In particular, Low-Rank Adaptation (LoRA) is one of the prominent PEFT methods. To this end, we introduce a novel method that exploits…
The growing size of large language models (LLMs) requires parameter-efficient fine-tuning (PEFT) methods for their adaptation to new tasks. Existing methods, such as Low-Rank Adaptation (LoRA), typically involve model adaptation by training the PEFT parameters. One open problem required to be solved…