no code implementations • 5 Apr 2022 • An-Chieh Cheng, Xueting Li, Sifei Liu, Min Sun, Ming-Hsuan Yang
With the capacity of modeling long-range dependencies in sequential data, transformers have shown remarkable performances in a variety of generative tasks such as image, audio, and text generation.
no code implementations • NeurIPS 2021 • An-Chieh Cheng, Xueting Li, Min Sun, Ming-Hsuan Yang, Sifei Liu
We propose a canonical point autoencoder (CPAE) that predicts dense correspondences between 3D shapes of the same category.
no code implementations • NeurIPS 2020 • Hung-Jen Chen, An-Chieh Cheng, Da-Cheng Juan, Wei Wei, Min Sun
To preserve the knowledge we learn from previous instances, we proposed a method to protect the path by restricting the gradient updates of one instance from overriding past updates calculated from previous instances if these instances are not similar.
2 code implementations • 26 Nov 2018 • An-Chieh Cheng, Chieh Hubert Lin, Da-Cheng Juan, Wei Wei, Min Sun
Conventional Neural Architecture Search (NAS) aims at finding a single architecture that achieves the best performance, which usually optimizes task related learning objectives such as accuracy.
no code implementations • 9 Sep 2018 • Hsuan-Kung Yang, An-Chieh Cheng, Kuan-Wei Ho, Tsu-Jui Fu, Chun-Yi Lee
The additional depth prediction path supplements the relationship prediction model in a way that bounding boxes or segmentation masks are unable to deliver.
no code implementations • 29 Aug 2018 • An-Chieh Cheng, Jin-Dong Dong, Chi-Hung Hsu, Shu-Huan Chang, Min Sun, Shih-Chieh Chang, Jia-Yu Pan, Yu-Ting Chen, Wei Wei, Da-Cheng Juan
Recent breakthroughs in Neural Architectural Search (NAS) have achieved state-of-the-art performance in many tasks such as image classification and language understanding.
no code implementations • ECCV 2018 • Jin-Dong Dong, An-Chieh Cheng, Da-Cheng Juan, Wei Wei, Min Sun
We propose DPP-Net: Device-aware Progressive Search for Pareto-optimal Neural Architectures, optimizing for both device-related (e. g., inference time and memory usage) and device-agnostic (e. g., accuracy and model size) objectives.