Adaptive Convolutional Kernels

The quest for increased computer vision recognition performance has led to the development of high complexity neural network architectures, each time with evolving deeper topologies. To avoid high computing resource requirements of such complex networks, and to enable operation on devices with limited resources, this work introduces the concept of adaptive kernels applied to convolutional layers. Motivated by the non-linear perception response in human visual cells, the input image is used to define the weights of a dynamically changing kernel, named adaptive kernel. This novel adaptive kernel is used to perform a second convolution operation over the input image in order to generate the output features. Adaptive kernels enable accurate recognition with significant lower memory requirements; this is accomplished by reducing the number of kernels and the number of layers needed as compared to typical CNN configurations. Additionally, the use of adaptive kernels allow the decrease by 2X the number of epochs required for training, and the number of activation function computations. Our experimental results show a reduction of 66X of the parameters needed of a CNN compared to LeNet when evaluated with the MNIST dataset, maintaining >99% of accuracy. Additionally, when using adaptive kernels implemented in a ResNet18, we observed a higher performance when compared to a known ResNet100 reported in the literature for CIFAR10 and it also gets better accuracy for ImageNet database.