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Searching Toward Pareto-Optimal Device-Aware Neural Architectures
Recent breakthroughs in Neural Architectural Search (NAS) have achieved state-of-the-art performance in many tasks such as image classification and language understanding.
DPP-Net: Device-aware Progressive Search for Pareto-optimal Neural Architectures
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.
Cube Padding for Weakly-Supervised Saliency Prediction in 360° Videos
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.
Cube Padding for Weakly-Supervised Saliency Prediction in 360° Videos
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.
