A pilot study of novel multi-filter CNN layer.

Convolutional neural networks (CNNs) have reached their peak of complex structures, but until now, few researchers have addressed the problem of relying on one filter size. Mainly a 3 × 3 filter is the most common one used in any structure. Only at the first layers of the CNN model, filters bigger than 3 × 3 could be partially used. Most researchers work with filters (size, values, etc.) as a black box. To the best of our knowledge, this research is the first pilot study that proposes a new multi-filter layer in which different filters with variant sizes are used to replace the 3 × 3 filter layers. Our proposed multi-filter layer has yielded encouraging results, demonstrating notable improvements ranging from 1% to 5% in performance. This achievement was realized by developing two innovative structures, namely the fixed structure and the decreasing structure. Both of them leverage the multi-filter layer. Although the two structures exhibit promising outcomes, the later structure offers the additional advantages of reduced computational requirements and enhanced learner strength.