1 code implementation • 14 Feb 2023 • Arpit Bansal, Hong-Min Chu, Avi Schwarzschild, Soumyadip Sengupta, Micah Goldblum, Jonas Geiping, Tom Goldstein
Typical diffusion models are trained to accept a particular form of conditioning, most commonly text, and cannot be conditioned on other modalities without retraining.
3 code implementations • 19 Aug 2022 • Arpit Bansal, Eitan Borgnia, Hong-Min Chu, Jie S. Li, Hamid Kazemi, Furong Huang, Micah Goldblum, Jonas Geiping, Tom Goldstein
We observe that the generative behavior of diffusion models is not strongly dependent on the choice of image degradation, and in fact an entire family of generative models can be constructed by varying this choice.
1 code implementation • 25 Nov 2021 • Zeyad Ali Sami Emam, Hong-Min Chu, Ping-Yeh Chiang, Wojciech Czaja, Richard Leapman, Micah Goldblum, Tom Goldstein
Active learning (AL) algorithms aim to identify an optimal subset of data for annotation, such that deep neural networks (DNN) can achieve better performance when trained on this labeled subset.
no code implementations • ICLR 2021 • Renkun Ni, Hong-Min Chu, Oscar Castaneda, Ping-Yeh Chiang, Christoph Studer, Tom Goldstein
Low-precision neural networks represent both weights and activations with few bits, drastically reducing the multiplication complexity.
no code implementations • 26 Jul 2020 • Renkun Ni, Hong-Min Chu, Oscar Castañeda, Ping-Yeh Chiang, Christoph Studer, Tom Goldstein
Low-resolution neural networks represent both weights and activations with few bits, drastically reducing the multiplication complexity.
no code implementations • ECCV 2018 • Hong-Min Chu, Chih-Kuan Yeh, Yu-Chiang Frank Wang
In order to train learning models for multi-label classification (MLC), it is typically desirable to have a large amount of fully annotated multi-label data.
1 code implementation • 5 Feb 2018 • Yao-Yuan Yang, Yi-An Lin, Hong-Min Chu, Hsuan-Tien Lin
Extracting the hidden correlation is generally a challenging task.
no code implementations • 14 Nov 2017 • Hong-Min Chu, Kuan-Hao Huang, Hsuan-Tien Lin
The foundation of CS-DPP is an online LSDR framework derived from a leading LSDR algorithm.
no code implementations • 2 Aug 2016 • Hong-Min Chu, Hsuan-Tien Lin
Empirical studies demonstrate that the learned experience not only is competitive with existing strategies on most single datasets, but also can be transferred across datasets to improve the performance on future learning tasks.