Balanced Mixture of SuperNets for Learning the CNN Pooling Architecture (2306.11982v1)

Abstract: Downsampling layers, including pooling and strided convolutions, are crucial components of the convolutional neural network architecture that determine both the granularity/scale of image feature analysis as well as the receptive field size of a given layer. To fully understand this problem, we analyse the performance of models independently trained with each pooling configurations on CIFAR10, using a ResNet20 network, and show that the position of the downsampling layers can highly influence the performance of a network and predefined downsampling configurations are not optimal. Network Architecture Search (NAS) might be used to optimize downsampling configurations as an hyperparameter. However, we find that common one-shot NAS based on a single SuperNet does not work for this problem. We argue that this is because a SuperNet trained for finding the optimal pooling configuration fully shares its parameters among all pooling configurations. This makes its training hard, because learning some configurations can harm the performance of others. Therefore, we propose a balanced mixture of SuperNets that automatically associates pooling configurations to different weight models and helps to reduce the weight-sharing and inter-influence of pooling configurations on the SuperNet parameters. We evaluate our proposed approach on CIFAR10, CIFAR100, as well as Food101 and show that in all cases, our model outperforms other approaches and improves over the default pooling configurations.

• The paper proposes a novel balanced mixture of SuperNets that mitigates weight-sharing issues in one-shot NAS for CNN pooling configurations.
• Exhaustive benchmarking on 36 pooling setups shows improvements, with accuracy on CIFAR10 rising from 90.52% to 92.01% using optimized configurations.
• The approach demonstrates broad applicability by achieving superior performance across multiple datasets and paving the way for advanced NAS techniques.

An Analysis of "Balanced Mixture of SuperNets for Learning the CNN Pooling Architecture"

The paper "Balanced Mixture of SuperNets for Learning the CNN Pooling Architecture," authored by M. Javan, M. Toews, and M. Pedersoli, provides a detailed evaluation of convolutional neural network (CNN) downsampling architectures, specifically concentrating on the optimization of pooling configurations. The authors propose a novel approach that integrates a balanced mixture of SuperNets to effectively improve CNN performance beyond the standard configurations.

Summary of Contributions

The central contribution of this paper is the introduction of a balanced mixture of SuperNets to mitigate the weight-sharing problem inherent in one-shot neural architecture search (NAS) methods, especially when applied to CNN pooling configurations. Key contributions include:

• Benchmarking Pooling Configurations: The authors conduct exhaustive experiments, training all 36 possible pooling configurations of a small ResNet model on the CIFAR10 dataset. This comprehensive analysis highlights that standard configurations are suboptimal, with the best configuration achieving 92.01% accuracy, compared to 90.52% for the default setup.
• Balanced Mixture of SuperNets: The paper proposes using multiple SuperNet models to reduce the detrimental inter-influence of different pooling configurations on shared network parameters. This innovative approach involves dynamically associating pooling configurations with distinct models, thereby allowing specialization and enhancing performance.
• Demonstrated Improvements: The authors validate their method on multiple datasets, including CIFAR10, CIFAR100, and Food101, demonstrating superior performance relative to traditional methods. When employed with various mixtures of SuperNets (M=[1,2,4,8]), the proposed method consistently outperformed each baseline configuration.

Technical Insights

The authors critically address the inadequacies of traditional NAS methods like DARTS and SPOS in reliably determining optimal pooling configurations. They argue convincingly that full weight-sharing between pooling configurations leads to inferior network performance. Their methodology includes:

• Differentiated Configuration Sampling: Unlike common NAS methods, which integrate all pooling configurations into a single SuperNet, this approach employs multiple SuperNets, each capable of excelling in different configurations based on learned associations. This configuration flexibility is managed via a learned probability distribution influenced by validation accuracy.
• Balanced Training across Models: The iterative proportional fitting strategy ensures that each model within the SuperNet mixture maintains equal training emphasis, thus preventing under-training of potentially beneficial configurations.

Implications and Future Directions

The research offers significant implications for both practical applications and theoretical advancements in CNN design. Practically, the enhanced ability to fine-tune pooling configurations based on the dataset can lead to more efficient and accurate CNN models across diverse domains, including image classification and beyond.

Theoretically, the work opens avenues for exploration in NAS methodologies that can benefit from reduced weight-sharing. The insights provided by this research invite further investigation into more granular NAS techniques tailored to other architectural features, such as fully connected layers or activation functions.

Future work may focus on extending this balanced SuperNet approach to a greater variety of neural network architectures and tasks, integrating these techniques with continual learning paradigms, or utilizing this methodology in more dynamic contexts, such as real-time data processing systems.

Conclusion

"Balanced Mixture of SuperNets for Learning the CNN Pooling Architecture" presents a substantial contribution to optimizing CNN architectures through innovative NAS techniques. The paper provides an insightful critique of current one-shot NAS methods and establishes a coherent framework for improving model accuracy through more intelligent resource and parameter allocation across architectures. With its combination of empirical rigor and methodological innovation, this paper sets a precedent for future research in neural architecture discovery and optimization.