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Found 4 papers, 2 papers with code
Towards More Efficient and Effective Inference: The Joint Decision of Multi-Participants
Existing approaches to improve the performances of convolutional neural networks by optimizing the local architectures or deepening the networks tend to increase the size of models significantly.
Rethinking the Number of Channels for the Convolutional Neural Network
In this paper, we propose a method for efficient automatic architecture search which is special to the widths of networks instead of the connections of neural architecture.
Gated Convolutional Networks with Hybrid Connectivity for Image Classification
We propose a simple yet effective method to reduce the redundancy of DenseNet by substantially decreasing the number of stacked modules by replacing the original bottleneck by our SMG module, which is augmented by local residual.
Ranked #60 on
Image Classification
on CIFAR-10
EENA: Efficient Evolution of Neural Architecture
Latest algorithms for automatic neural architecture search perform remarkable but are basically directionless in search space and computational expensive in training of every intermediate architecture.
