SkipNet: Learning Dynamic Routing in Convolutional Networks (1711.09485v2)

Abstract: While deeper convolutional networks are needed to achieve maximum accuracy in visual perception tasks, for many inputs shallower networks are sufficient. We exploit this observation by learning to skip convolutional layers on a per-input basis. We introduce SkipNet, a modified residual network, that uses a gating network to selectively skip convolutional blocks based on the activations of the previous layer. We formulate the dynamic skipping problem in the context of sequential decision making and propose a hybrid learning algorithm that combines supervised learning and reinforcement learning to address the challenges of non-differentiable skipping decisions. We show SkipNet reduces computation by 30-90% while preserving the accuracy of the original model on four benchmark datasets and outperforms the state-of-the-art dynamic networks and static compression methods. We also qualitatively evaluate the gating policy to reveal a relationship between image scale and saliency and the number of layers skipped.

• The paper introduces dynamic routing using gating networks and reinforcement learning to selectively skip CNN layers for efficiency.
• It employs a reinforcement learning framework that optimizes layer skipping and achieves up to 30% reduction in FLOPs on benchmark datasets.
• The method maintains high classification accuracy while reducing computational overhead, benefiting deployment on resource-constrained devices.

SkipNet: Learning Dynamic Routing in Convolutional Networks

The paper "SkipNet: Learning Dynamic Routing in Convolutional Networks" by Xin Wang, Fisher Yu, Zi-Yi Dou, Trevor Darrell, and Joseph E. Gonzalez introduces an innovative approach to enhancing the efficiency of Convolutional Neural Networks (CNNs) through dynamic routing strategies. The authors present skip connections that adaptively learn to bypass specific network layers, optimizing both the computational overhead and the CNNs' ability to generalize.

Overview

The primary contribution of SkipNet is the development of a dynamic routing mechanism that facilitates the selective execution of network layers. This approach not only accelerates inference times but also preserves model accuracy, addressing a common trade-off in deep learning models. The research leverages reinforcement learning to train a policy network that dynamically determines which layers to skip during the prediction phase.

Technical Contributions

1. Dynamic Routing via Gating Networks: SkipNet integrates gating networks into standard CNN architectures. These gating networks are responsible for making binary decisions—to skip or execute—aiding layer selection during inference.
2. Reinforcement Learning: A reinforcement learning framework is utilized to optimize the gating function. This framework circumvents the need for manually configuring layer skips, allowing the model to learn optimal configurations in a data-driven manner.
3. Empirical Evaluation: The authors conducted extensive experiments on well-known benchmarks such as CIFAR-10 and ImageNet. SkipNet demonstrated substantial improvements in computational efficiency, achieving up to 30% reduction in FLOPs with negligible impact on classification performance.

Numerical Results

Significant numerical results in the paper indicate that SkipNet achieves a marked reduction in computational requirements without compromising accuracy. For example, on the CIFAR-10 dataset, SkipNet achieves the same level of accuracy as ResNet with 30% fewer computations. This efficiency is particularly valuable for deploying models in resource-constrained environments such as mobile and embedded systems.

Implications and Future Directions

The implications of this research are two-fold. Practically, SkipNet offers a viable solution for deploying deep learning models on devices with limited computational capabilities. Theoretically, the paper provides insights into dynamic model architectures, paving the way for more intelligent and adaptive neural networks.

Future developments may focus on extending the SkipNet framework to other domain-specific architectures, such as recurrent neural networks or transformers, where similar dynamic routing principles could yield efficiency gains. Additionally, investigating the stability and convergence properties of reinforcement learning-based routing strategies could further solidify the theoretical underpinnings proposed in this paper.

In conclusion, SkipNet addresses a critical challenge in CNN deployment by dynamically managing computational resources, achieving efficiency without sacrificing performance. This framework's contributions underscore the potential of dynamic architectures in the ongoing evolution of deep learning technologies.