9.7k citations · 22.1k across the 16 of their papers we have counts for
10 papers · 1 filter
ChamNet: Towards Efficient Network Design through Platform-Aware Model Adaptation
Xiaoliang Dai, Peizhao Zhang, Bichen Wu +10
This paper proposes an efficient neural network (NN) architecture design methodology called Chameleon that honors given resource constraints. Instead of developing new building blo…
FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search
Bichen Wu, Xiaoliang Dai, Peizhao Zhang +7
Designing accurate and efficient ConvNets for mobile devices is challenging because the design space is combinatorially large. Due to this, previous neural architecture search (NAS…
Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
Priya Goyal, Piotr Dollár, Ross Girshick +6
Deep learning thrives with large neural networks and large datasets. However, larger networks and larger datasets result in longer training times that impede research and developme…
Combining the Best of Convolutional Layers and Recurrent Layers: A Hybrid Network for Semantic Segmentation
Zhicheng Yan, Hao Zhang, Yangqing Jia +2
State-of-the-art results of semantic segmentation are established by Fully Convolutional neural Networks (FCNs). FCNs rely on cascaded convolutional and pooling layers to gradually…
Going Deeper with Convolutions
Christian Szegedy, Wei Liu, Yangqing Jia +6
We propose a deep convolutional neural network architecture codenamed "Inception", which was responsible for setting the new state of the art for classification and detection in th…
Caffe: Convolutional Architecture for Fast Feature Embedding
Yangqing Jia, Evan Shelhamer, Jeff Donahue +5
Caffe provides multimedia scientists and practitioners with a clean and modifiable framework for state-of-the-art deep learning algorithms and a collection of reference models. The…