Hybrid of DiffStride and Spectral Pooling in Convolutional Neural
  Networks
Sulthan Rafif Mochamad Arfan Ravy Wahyu Pratama Mohammad Faris Azhar Ahmad Mustafidul Ibad Lailil Muflikhah Novanto Yudistira
Stride determines the distance between adjacent filter positions as the
filter moves across the input. A fixed stride causes important information
contained in the image can not be captured, so that important information is
not classified. Therefore, in previous research, the DiffStride Method was
applied, namely the Strided Convolution Method with which it can learn its own
stride value. Severe Quantization and a constraining lower bound on preserved
information are arises with Max Pooling Downsampling Method. Spectral Pooling
reduce the constraint lower bound on preserved information by cutting off the
representation in the frequency domain. In this research a CNN Model is
proposed with the Downsampling Learnable Stride Technique performed by
Backpropagation combined with the Spectral Pooling Technique. Diffstride and
Spectral Pooling techniques are expected to maintain most of the information
contained in the image. In this study, we compare the Hybrid Method, which is a
combined implementation of Spectral Pooling and DiffStride against the Baseline
Method, which is the DiffStride implementation on ResNet 18. The accuracy
result of the DiffStride combination with Spectral Pooling improves over
DiffStride which is baseline method by 0.0094. This shows that the Hybrid
Method can maintain most of the information by cutting of the representation in
the frequency domain and determine the stride of the learning result through
Backpropagation.