# Training CNNs with Selective Allocation of Channels

arXiv: https://arxiv.org/abs/1905.04509 · HTML: https://mlsanity.com/paper/1905.04509/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2019.

## Built on by (most cited first, 5 of 5)

- [State Entropy Maximization with Random Encoders for Efficient Exploration](https://mlsanity.com/paper/2102.09430/) (2021)
- [Improving Computational Efficiency in Visual Reinforcement Learning via Stored Embeddings](https://mlsanity.com/paper/2103.02886/) (2021)
- [Dynamic Shuffle: An Efficient Channel Mixture Method](https://mlsanity.com/paper/2310.02776/) (2023)
- [Self-Reorganizing and Rejuvenating CNNs for Increasing Model Capacity Utilization](https://mlsanity.com/paper/2102.06870/) (2021)
- [Weight Evolution: Improving Deep Neural Networks Training through Evolving Inferior Weight Values](https://mlsanity.com/paper/2110.04492/) (2021)

## Abstract (arXiv)

Recent progress in deep convolutional neural networks (CNNs) have enabled a simple paradigm of architecture design: larger models typically achieve better accuracy. Due to this, in modern CNN architectures, it becomes more important to design models that generalize well under certain resource constraints, e.g. the number of parameters. In this paper, we propose a simple way to improve the capacity of any CNN model having large-scale features, without adding more parameters. In particular, we modify a standard convolutional layer to have a new functionality of channel-selectivity, so that the layer is trained to select important channels to re-distribute their parameters. Our experimental results under various CNN architectures and datasets demonstrate that the proposed new convolutional layer allows new optima that generalize better via efficient resource utilization, compared to the baseline.