Paper Efficient and Degradation-Adaptive Network for Real-World Image Super-Resolution Jie Liang*, Hui Zeng*, and Lei Zhang. In arxiv preprint. Abstract Efficient and effective real-world image super-resolution (Real-ISR) is a challenging task due to the unknown complex degradation of real-world images and the limited computation resources in practical applications. Recent research on Real-ISR has achieved significant progress by modeling the image degradation space; however, these methods largely rely on heavy backbone networks and they are inflexible to handle images of different […]

LoRA This repo contains the implementation of LoRA in GPT-2 and steps to replicate the results in our recent paper LoRA: Low-Rank Adaptation of Large Language ModelsEdward J. Hu*, Yelong Shen*, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Weizhu ChenPaper: https://arxiv.org/abs/2106.09685 LoRA reduces the number of trainable parameters by learning pairs of rank-decompostion matrices and freezing the original weights. This vastly reduces the storage requirement for large language models adapted to specific tasks and enables efficient task-switching during deployment […]