Foundation models, now powering most of the exciting applications in deep learning, are almost universally based on the Transformer architecture and its core attention module.
Reinforcement Learning (RL) offers a versatile framework for achieving long-term goals.
High-resolution image generation with Generative Artificial Intelligence (GenAI) has immense potential but, due to the enormous capital investment required for training, it is increasingly centralised to a few large corporations, and hidden behind paywalls.
During generation, RCG samples from such representation distribution using a representation diffusion model (RDM), and employs a pixel generator to craft image pixels conditioned on the sampled representation.
Ranked #1 on Unconditional Image Generation on ImageNet 256x256
Magicoder models are trained on 75K synthetic instruction data using OSS-Instruct, a novel approach to enlightening LLMs with open-source code snippets to generate high-quality instruction data for code.
Large-scale Text-to-Image (T2I) models have rapidly gained prominence across creative fields, generating visually compelling outputs from textual prompts.
We introduce a novel sequential modeling approach which enables learning a Large Vision Model (LVM) without making use of any linguistic data.
Monocular depth estimation is a fundamental computer vision task.