Paper:
Deep neural networks have become the state-of-the-art models in numerous machine learning tasks. However, general guidance to network architecture design is still missing. In our work, we bridge deep neural network design with numerical differential equations. We show that many effective networks, such as ResNet, PolyNet, FractalNet and RevNet, can be interpreted as different numerical discretizations of differential equations. This finding brings us a brand new perspective on the design of effective deep architectures. We can take advantage of the rich knowledge in numerical analysis to guide us in designing new and potentially more effective deep networks. As an example, we propose a linear multi-step architecture (LM-architecture) which is inspired by the linear multi-step method solving ordinary differential equations. The LM-architecture is an effective structure that can be used on any ResNet-like networks. In particular, we demonstrate that LM-ResNet and LM-ResNeXt (i.e. the networks obtained by applying the LM-architecture on ResNet and ResNeXt respectively) can achieve noticeably higher accuracy than ResNet and ResNeXt on both CIFAR and ImageNet with comparable numbers of trainable parameters. In particular, on both CIFAR and ImageNet, LM-ResNet/LM-ResNeXt can significantly compress (> 50%) the original networks while maintaining a similar performance. This can be explained mathematically using the concept of modified equation from numerical analysis. Last but not least, we also establish a connection between stochastic control and noise injection in the training process which helps to improve generalization of the networks. Furthermore, by relating stochastic training strategy with stochastic dynamic system, we can easily apply stochastic training to the networks with the LM-architecture. As an example, we introduced stochastic depth to LM-ResNet and achieve significant improvement over the original LM-ResNet on CIFAR10.

Review:
Summary
- This paper draws analogy from numerical differential equation solvers and popular residual network-like deep learning architectures. It makes connection from ResNet, FractalNet, DenseNet, and RevNet to different numerical solvers such as forward and backward Euler and Runge-Kunta. In addition, inspired by the Linear Multi-step methods (LM), the authors propose a novel LM-ResNet architecture in which the next residual block takes a linear combination of the previous two residual blocks’ activations. They also propose a stochastic version of LM-ResNet that resembles Shake-shake regularization and stochastic depth. In both deterministic and stochastic cases, they show a positive improvement in classification accuracy on standard object classification benchmarks such as CIFAR-10/100 and ImageNet.
Pros
- The intuition is good that connects differential equation and ResNet-like architecture, also explored in some of the related work.
- Building upon the intuition, the author proposes a novel architecture based on a numerical ODE solver method.
- Consistent improvement in accuracy is observed in both deterministic and stochastic cases.
Cons
- The title is a little bit misleading. “Beyond Finite Layer Neural Networks” sounds like the paper proposes some infinite layer neural networks but the paper only studies finite number of layers.
- One thing that needs to be clarified is that, if the network is not targeted at solving certain ODEs, then why is the intuition from ODE matters? The paper does not motivate readers in this perspective.
- Given the widespread use of ResNet in the vision community, the incremental improvement of 1% on ImageNet is less likely to push vision research to switch to a completely different architecture. Therefore, the potential impact of the this paper to vision community is probably limited.
Conclusion
- Based on the comments above, I think the paper is a good contribution which links ODE with Deep Networks and derivation is convincing. The proposed new architecture can be considered in future architecture designs. Although the increase in performance is small, I think it is good enough to accept.