Convolutional Neural Networks at Constrained Time Cost

Abstract

Though recent advanced convolutional neural networks (CNNs) have been improving the image recognition accuracy, the models are getting more complex and time-consuming. For real-world applications in industrial and commercial scenarios, engineers and developers are often faced with the requirement of constrained time budget. In this paper, we investigate the accuracy of CNNs under constrained time cost. Under this constraint, the designs of the network architectures should exhibit as trade-offs among the factors like depth, numbers of filters, filter sizes, etc. With a series of controlled comparisons, we progressively modify a baseline model while preserving its time complexity. This is also helpful for understanding the importance of the factors in network designs. We present an architecture that achieves very competitive accuracy in the ImageNet dataset (11.8% top-5 error, 10-view test), yet is 20% faster than "AlexNet" (16.0% top-5 error, 10-view test).

8-page technical report

References in corpus (9)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Caffe: Convolutional Architecture for Fast Feature Embedding
• Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
• Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation
• Going Deeper with Convolutions
• One weird trick for parallelizing convolutional neural networks
• Return of the Devil in the Details: Delving Deep into Convolutional Nets
• Speeding up Convolutional Neural Networks with Low Rank Expansions
• Simultaneous Detection and Segmentation

Cited by in corpus (6)

• Automatic Ship Detection of Remote Sensing Images from Google Earth in Complex Scenes Based on Multi-Scale Rotation Dense Feature Pyramid Networks
• Image Super-Resolution Using Deep Convolutional Networks
• Vertex and Energy Reconstruction in JUNO with Machine Learning Methods
• An Experimental Study of the Impact of Pre-training on the Pruning of a Convolutional Neural Network
• VIPLFaceNet: An Open Source Deep Face Recognition SDK
• SuperNet -- An efficient method of neural networks ensembling