all AI news
CLoRA: A Contrastive Approach to Compose Multiple LoRA Models
April 1, 2024, 4:42 a.m. | Tuna Han Salih Meral, Enis Simsar, Federico Tombari, Pinar Yanardag
cs.LG updates on arXiv.org arxiv.org
Abstract: Low-Rank Adaptations (LoRAs) have emerged as a powerful and popular technique in the field of image generation, offering a highly effective way to adapt and refine pre-trained deep learning models for specific tasks without the need for comprehensive retraining. By employing pre-trained LoRA models, such as those representing a specific cat and a particular dog, the objective is to generate an image that faithfully embodies both animals as defined by the LoRAs. However, the task …
abstract adapt arxiv cs.cv cs.lg deep learning image image generation lora low multiple popular refine retraining specific tasks tasks type
More from arxiv.org / cs.LG updates on arXiv.org
Sliced Wasserstein with Random-Path Projecting Directions
1 day, 19 hours ago |
arxiv.org
Learning Extrinsic Dexterity with Parameterized Manipulation Primitives
1 day, 19 hours ago |
arxiv.org
The Un-Kidnappable Robot: Acoustic Localization of Sneaking People
1 day, 19 hours ago |
arxiv.org
Jobs in AI, ML, Big Data
Data Engineer
@ Lemon.io | Remote: Europe, LATAM, Canada, UK, Asia, Oceania
Artificial Intelligence – Bioinformatic Expert
@ University of Texas Medical Branch | Galveston, TX
Lead Developer (AI)
@ Cere Network | San Francisco, US
Research Engineer
@ Allora Labs | Remote
Ecosystem Manager
@ Allora Labs | Remote
Founding AI Engineer, Agents
@ Occam AI | New York