## 1 Introduction
DL methods have been successfully applied to image classification [12; 13; 14], speech recognition and audio processing [15; 16], music classification and analysis [17; 18; 19], environmental sound classification [20], and bioinformatics [21; 22]. Convolutional neural networks (ConvNets) are DL feedforward neural networks that have been shown to generalize well on large quantities of digital data [23]. In ConvNets, filters of various sizes are convolved with a raw input signal to obtain a stack of filtered signals. This transformation is called a _convolutional layer_. The size of this layer is equal to the number of convolutional filters. The convolved signals are typically normalized. A standard normalization choice is non-linear activation functions such as the rectified linear unit (ReLU) that converts all negative values to 0s [24; 25; 26]. The size of the output signal from a layer can be downsampled with a _maxpooling layer_, where a window is shifted in steps, called _strides_, across the input signal retaining the maximum value from each window. A _fully connected_ (FC) layer treats a stack of processed signals as a 1D vector so that each value in the output from a previous layer is connected via a _synapse_ (a weighted connection that transmits a value from one unit to another) to each node in the next layer. In addition to these layers, there can be custom layers that implement arbitrary functions to transform signals between consecutive layers. [PERSON], [PERSON], and [PERSON] [12] did the ground breaking research on applying deep ConvNet architectures to large image datasets that rekindled interest in deep neural networks. The researchers trained a deep ConvNet to classify 1.2 million images in the ImageNet LSVRC-2010 content into 1000 different classes. The network consisted of five convolutional layers, some of which were followed by maxpooling layers, and three FC layers with a final 1000-way softmax layer and dropouts [27] of various rates. The network consisted of 60 million parameters and included 650,000 neurons. Variants of this architecture still dominate the image classification literature and have so far yielded the best results on such publicly available datasets as MNIST [23], CIFAR [28], and ImageNet [29]. A standard way to improve the performance of deep ConvNets is by increasing the number of layers and the number of neurons in each layer. For example, [PERSON] et al. [30] proposed a deep ConvNet architecture, called _Inception_, that improves the utilization of the computational resources inside the network. The architecture is implemented in GoogLeNet, a deep network with 22 layers. This network approximates the expected optimal sparse structure by using dense building blocks available within the network. A recent development in the state of the art object recognition with ConvNets is the Regions with ConvNets (R-CNN) [31; 32]. Given an image, an R-CNN system uses a class-independent object location method (e.g., selective search [33]) to generate a set of region proposals, computes a set of features for each region with a ConvNet, and then classifies these features with a class-specific classifier such as a support-vector machine (SVM) or another ConvNet. YOLO [34] is an object detection system that, instead of applying trained models to multiple locations and scoring them for objects, applies a single neural network to entire images. The network itself divides the image into sub-images and calculates bounding boxes and probabilities for different locations. SSD [35] is another approach to object classification by neural networks that starts with a set of pre-computed, fixed size bounding boxes (called priors) that closely match the distribution of the ground truth object boxes and regresses the priors to the ground truth boxes. Tiny SSD [36] is a recent development of the SSD multi-box approach. It is a deep ConvNet based on SSD for real-time embedded object detection.