Active Convolution: Learning the Shape of Convolution for Image Classification (1703.09076v1)

Abstract: In recent years, deep learning has achieved great success in many computer vision applications. Convolutional neural networks (CNNs) have lately emerged as a major approach to image classification. Most research on CNNs thus far has focused on developing architectures such as the Inception and residual networks. The convolution layer is the core of the CNN, but few studies have addressed the convolution unit itself. In this paper, we introduce a convolution unit called the active convolution unit (ACU). A new convolution has no fixed shape, because of which we can define any form of convolution. Its shape can be learned through backpropagation during training. Our proposed unit has a few advantages. First, the ACU is a generalization of convolution; it can define not only all conventional convolutions, but also convolutions with fractional pixel coordinates. We can freely change the shape of the convolution, which provides greater freedom to form CNN structures. Second, the shape of the convolution is learned while training and there is no need to tune it by hand. Third, the ACU can learn better than a conventional unit, where we obtained the improvement simply by changing the conventional convolution to an ACU. We tested our proposed method on plain and residual networks, and the results showed significant improvement using our method on various datasets and architectures in comparison with the baseline.

• The paper introduces the Active Convolution Unit (ACU) which learns dynamic receptive field shapes during training, generalizing traditional fixed convolutions.
• Experiments showed integrating ACUs improved error rates on CIFAR by up to 0.74% and top-5 accuracy on Place365 by up to 0.79%.
• The ACU represents a paradigm shift towards adaptive feature extraction, enabling CNNs to learn tailored spatial features with potential for future advancements.

Overview of Active Convolution: Learning the Shape of Convolution for Image Classification

The paper presents an innovative advancement in convolutional neural networks (CNNs) by introducing the Active Convolution Unit (ACU). Unlike traditional convolution layers with fixed receptive field shapes, the ACU allows for these shapes to be dynamically learned during training, offering significant improvements in image classification tasks. This research transitions the emphasis from architectural engineering to refining convolution units themselves, marking a promising shift in deep learning methodologies.

The ACU provides several advantages. Firstly, it generalizes traditional convolutions, accommodating any configuration by learning sub-pixel and fractional shapes. This flexibility extends CNN structures beyond static convolutions, enriching them with representational capacity. Secondly, the process eliminates manual tuning traditionally required, as the optimal convolution shape is automatically determined through backpropagation. Lastly, experiments demonstrate superior performance improvements on both plain and residual networks, showcasing efficacy across different datasets.

Experimental Results and Numerical Analysis

The implementation of ACU demonstrated marked improvements in classification accuracy metrics across diverse network architectures. On CIFAR-10 and CIFAR-100 datasets, the integration of ACU into plain networks led to decrease in error rates by 0.68% and 0.74% respectively, over conventional convolution methods. Further examination with residual networks—known for their role in facilitating deeper layers—showed error rate reductions of 0.47% for a basic residual setup and 0.52% for bottleneck structures on CIFAR-10 dataset, evidencing substantial gains.

In applied settings with larger datasets, such as Place365, where the breadth of the image set offers more variance in visual features, ACUs in both AlexNet and residual network setups improved top-5 accuracy by 0.79% and 0.49% respectively. The numerical results echo the versatility and robustness of ACU in scaling to more extensive and complex datasets beyond controlled experimental frameworks.

Theoretical Implications and Future Directions

The transformative capacity of the ACU introduces an intriguing paradigm shift in convolutional layer development. By internalizing position parameter learning, the ACU promotes an adaptive feature extraction process innately tailored to the image data being processed. The resulting synaptic movement across neuron space, reminiscent of biological neural computation mechanisms, facilitates a nuanced approach to spatial feature learning.

Looking forward, the generalization to continuous input spaces and the documented benefits of ACUs invite further exploration into potentially greater levels of abstraction, such as hierarchical position parameter learning. The flexibility inherent in ACU architecture posits further integration with advanced neural paradigms, catalyzing substantial developments in the efficiency of CNNs.

The paper concludes by acknowledging areas yet to be explored, such as the deployment of multiple sets of positions within single layers, which could potentially amplify the model’s representational power. When incorporated into current state-of-the-art AI systems, these insights might help drive the transition from mere data processing to a domain of more intelligent, responsive networks harboring predictive learning mechanisms finely tuned to their operational landscapes.