Convolutional Neural Networks with Alternately Updated Clique (1802.10419v3)

Abstract: Improving information flow in deep networks helps to ease the training difficulties and utilize parameters more efficiently. Here we propose a new convolutional neural network architecture with alternately updated clique (CliqueNet). In contrast to prior networks, there are both forward and backward connections between any two layers in the same block. The layers are constructed as a loop and are updated alternately. The CliqueNet has some unique properties. For each layer, it is both the input and output of any other layer in the same block, so that the information flow among layers is maximized. During propagation, the newly updated layers are concatenated to re-update previously updated layer, and parameters are reused for multiple times. This recurrent feedback structure is able to bring higher level visual information back to refine low-level filters and achieve spatial attention. We analyze the features generated at different stages and observe that using refined features leads to a better result. We adopt a multi-scale feature strategy that effectively avoids the progressive growth of parameters. Experiments on image recognition datasets including CIFAR-10, CIFAR-100, SVHN and ImageNet show that our proposed models achieve the state-of-the-art performance with fewer parameters.

• The paper introduces a novel CliqueNet architecture that employs bidirectional, alternately updated connections to enhance feature refinement in CNNs.
• The proposed design leverages multi-scale feature integration and parameter recycling, achieving competitive results on CIFAR-10, CIFAR-100, SVHN, and ImageNet.
• The architecture challenges conventional CNN designs by delivering high efficiency and scalability for resource-constrained applications.

Overview of "Convolutional Neural Networks with Alternately Updated Clique"

The paper "Convolutional Neural Networks with Alternately Updated Clique" introduces the CliqueNet architecture, a novel structure designed to enhance information flow within convolutional neural networks (CNNs). Unlike traditional CNNs, CliqueNet establishes bidirectional connections between layers within a single block, enabling a loop-based alternation of updates. This architecture facilitates enhanced feature refinement through recurrent feedback mechanisms.

Key Features and Architecture

CliqueNet's architecture is characterized by several innovative components:

1. Bidirectional Connectivity: Each layer within a Clique Block is interconnected, functioning as both input and output. This allows for a maximization of information flow, offering a more densely connected structure than that seen in DenseNets.
2. Alternately Updated Layers: Layers undergo alternating updates, ensuring recurrent refinement. Newly updated layers contribute to the re-evaluation of previously updated layers, promoting enhanced spatial attention and improved feature representation.
3. Multi-Scale Feature Strategy: To prevent parameter inflation, a multi-scale feature strategy is used. Only the refined Stage-II features are propagated to subsequent blocks, maintaining computational efficiency while capitalizing on refined representations.
4. Parameter Reutilization: CliqueNet employs a parameter recycling strategy. This approach enables the network to extend its representational depth without a proportional increase in the parameter count.

Experimental Evaluation

The paper presents experiments conducted on CIFAR-10, CIFAR-100, SVHN, and ImageNet datasets. CliqueNet demonstrates competitive performance with state-of-the-art models while utilizing fewer parameters:

• On CIFAR-10 and CIFAR-100, CliqueNet achieves a significant reduction in error rates with notably fewer parameters.
• On SVHN, similar trends are observed, with CliqueNet outperforming many existing architectures.
• When tested on ImageNet, CliqueNet also shows competitive results, reinforcing its scalability to large datasets.

Implications and Future Work

CliqueNet's use of recurrent feedback and efficient parameter utilization presents multiple practical advantages:

• Theoretical Implications: The structure challenges traditional deep learning paradigms by proving that extensive depth and parameter count are not prerequisites for high performance. Instead, strategic connectivity and feedback can achieve comparable outcomes.
• Practical Implications: CliqueNet's reduced parameter footprint and enhanced feature refinement make it a suitable candidate for deployment in environments with limited computational resources.
• Potential for Broader Applications: The architecture's general design suggests applicability in various computer vision tasks, such as semantic segmentation and image captioning. Future research may explore these extensions.

Conclusion

This work introduces CliqueNet, a novel convolutional architecture that leverages alternately updated cliques to enhance learning efficiency and feature refinement. By maximizing information flow and implementing effective strategies like multi-scale feature integration, CliqueNet sets a new precedent in neural network architecture design. The paper's findings offer a significant contribution to the ongoing development of more efficient and effective neural networks.