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.
no code implementations • CVPR 2018 • Hsien-Tzu Cheng, Chun-Hung Chao, Jin-Dong Dong, Hao-Kai Wen, Tyng-Luh Liu, Min Sun
Then, we concatenate all six faces while utilizing the connectivity between faces on the cube for image padding (i. e., Cube Padding) in convolution, pooling, convolutional LSTM layers.
no code implementations • CVPR 2018 • Hsien-Tzu Cheng, Chun-Hung Chao, Jin-Dong Dong, Hao-Kai Wen, Tyng-Luh Liu, Min Sun
Then, we concatenate all six faces while utilizing the connectivity between faces on the cube for image padding (i. e., Cube Padding) in convolution, pooling, convolutional LSTM layers.