ImageNet Classification with Deep Convolutional Neural Networks
A deep convolutional network demonstrated how data, GPU computation and learned representations could transform visual recognition.
Robot control is formulated as conditional denoising of action sequences, linking diffusion models to visuomotor learning.
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A bridge from generative modeling to physical action.
@misc{chi2023diffusion,
title = {Diffusion Policy: Visuomotor Policy Learning via Action Diffusion},
author = {Cheng Chi and Zhenjia Xu and Siyuan Feng and Eric Cousineau and Yilun Du and Benjamin Burchfiel and Russ Tedrake and Shuran Song},
year = {2023},
eprint = {2303.04137},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2303.04137}
}Rights remain with the original authors. Refer to the original source for citations and licensing.
A deep convolutional network demonstrated how data, GPU computation and learned representations could transform visual recognition.
A generative model learns to reverse a gradual noise process, establishing an influential approach to image synthesis.
Contrastive training aligns images with natural-language descriptions, enabling transfer to new visual categories.