We Need Far Fewer Unique Filters Than We Thought

Zahra Babaiee, Peyman Kiasari, Daniela Rus, Radu Grosu

Abstract: We challenge the conventional belief that CNNs require numerous distinct kernels for effective image classification. Our study on depthwise separable CNNs (DS-CNNs) reveals that a drastically reduced set of unique filters can maintain performance. Replacing thousands of trained filters in ConvNextv2 with the closest linear transform from a small filter set, results in small accuracy drops. Remarkably, initializing depthwise filters with \textbf{only 8 unique frozen filters}, achieves minimal accuracy drop on ImageNet. Our findings question the necessity of numerous filters in DS-CNNs, offering insights into more efficient network designs.

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Submission Number: 62