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” [14] (16.0% top-5 error, 10-view test).
Keywords
Computer scienceConvolutional neural networkConstraint (computer-aided design)Time constraintArtificial intelligenceFilter (signal processing)Machine learningArtificial neural networkArchitectureBaseline (sea)Network architectureComputer engineeringComputer vision
Affiliated Institutions
Related Publications
Efficient and accurate approximations of nonlinear convolutional networks
This paper aims to accelerate the test-time computation of deep convolutional neural networks (CNNs). Unlike existing methods that are designed for approximating linear filters ...
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
We introduce an extremely computation-efficient CNN architecture named ShuffleNet, which is designed specially for mobile devices with very limited computing power (e.g., 10-150...
GhostNet: More Features From Cheap Operations
Deploying convolutional neural networks (CNNs) on embedded devices is difficult due to the limited memory and computation resources. The redundancy in feature maps is an importa...
Accelerating Very Deep Convolutional Networks for Classification and Detection
This paper aims to accelerate the test-time computation of convolutional neural networks (CNNs), especially very deep CNNs [1] that have substantially impacted the computer visi...
Dynamic Convolution: Attention Over Convolution Kernels
Light-weight convolutional neural networks (CNNs) suffer performance degradation as their low computational budgets constrain both the depth (number of convolution layers) and t...
Publication Info
- Year
- 2015
- Type
- preprint
- Citations
- 1487
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
1487
OpenAlex
Cite This
Kaiming He,
Jian Sun
(2015).
Convolutional neural networks at constrained time cost.
.
https://doi.org/10.1109/cvpr.2015.7299173
Identifiers
- DOI
- 10.1109/cvpr.2015.7299173