Deep Convolutional Neural Networks (CNNs) are a special type of Neural Networks, which have shown state-of-the-art performance on various competitive benchmarks. The powerful learning ability of deep CNN is largely due to the use of multiple feature extraction stages (hidden layers) that can automatically learn representations from the data. Availability of a large amount of data and improvements in the hardware processing units have accelerated the research in CNNs, and recently very interesting deep CNN architectures are reported. The recent race in developing deep CNNs shows that the innovative architectural ideas, as well as parameter optimization, can improve CNN performance. In this regard, different ideas in the CNN design have been explored such as the use of different activation and loss functions, parameter optimization, regularization, and restructuring of the processing units. However, the major improvement in representational capacity of the deep CNN is achieved by the restructuring of the processing units. Especially, the idea of using a block as a structural unit instead of a layer is receiving substantial attention. This survey thus focuses on the intrinsic taxonomy present in the recently reported deep CNN architectures and consequently, classifies the recent innovations in CNN architectures into seven different categories. These seven categories are based on spatial exploitation, depth, multi-path, width, feature map exploitation, channel boosting, and attention. Additionally, this survey also covers the elementary understanding of CNN components and sheds light on its current challenges and applications.

Keywords: Deep Learning, Convolutional Neural Networks, Architecture, Representational Capacity, Residual Learning, and Channel Boosted CNN.

        Machine Learning (ML) algorithms belong to a specialized area in Artificial Intelligence (AI), which endows intelligence to computers by learning the underlying relationships among the data and making decisions without being explicitly programmed. Different ML algorithms have been developed since the late 1990s, for the emulation of human sensory responses such as speech and vision, but they have generally failed to achieve human-level satisfaction [1]–[6]. The challenging nature of Machine Vision (MV) tasks gives rise to a specialized class of Neural Networks (NN), known as Convolutional Neural Network (CNN) [7].

     CNNs are considered as one of the best techniques for learning image content and have shown state-of-the-art results on image recognition, segmentation, detection, and retrieval related tasks [8], [9]. The success of CNN has captured attention beyond academia. In industry, companies such as Google, Microsoft, AT&T, NEC, and Facebook have developed active research groups for exploring new architectures of CNN [10]. At present, most of the frontrunners of image processing competitions are employing deep CNN based models.

        Various improvements in CNN learning strategy and architecture were performed to make CNN scalable to large and complex problems. These innovations can be categorized as parameter optimization, regularization, structural reformulation, etc. However, it is observed that CNN based applications became prevalent after the exemplary performance of AlexNet on ImageNet dataset [21]. Thus major innovations in CNN have been proposed since 2012 and were mainly due to restructuring of processing units and designing of new blocks. Similarly, Zeiler and Fergus [28] introduced the concept of layer-wise visualization of features, which shifted the trend towards extraction of features at low spatial resolution in deep architecture such as VGG [29]. Nowadays, most of the new architectures are built upon the principle of simple and homogenous topology introduced by VGG. On the other hand, Google group introduced an interesting idea of split, transform, and merge, and the corresponding block is known as inception block. The inception block for the very first time gave the concept of branching within a layer, which allows abstraction of features at different spatial scales [30]. In 2015, the concept of skip connections introduced by ResNet [31] for the training of deep CNNs got famous, and afterwards, this concept was used by most of the succeeding Nets, such as Inception-ResNet, WideResNet, ResNext, etc [32]–[34].

        In order to improve the learning capacity of a CNN, different architectural designs such as WideResNet, Pyramidal Net, Xception etc. explored the effect of multilevel transformations in terms of an additional cardinality and increase in width [32], [34], [35]. Therefore, the focus of research shifted from parameter optimization and connections readjustment towards improved architectural design (layer structure) of the network. This shift resulted in many new architectural ideas such as channel boosting, spatial and channel wise exploitation and attention based information processing etc. [36]–[38].

        Nowadays, CNN is considered as the most widely used ML technique, especially in vision related applications. CNNs have recently shown state-of-the-art results in various ML applications. A typical block diagram of an ML system is shown in Fig. 2. Since, CNN possesses both good feature extraction and strong discrimination ability, therefore in a ML system; it is mostly used for feature extraction and classification.

A typical CNN architecture generally comprises of alternate layers of convolution and pooling followed by one or more fully connected layers at the end. In some cases, fully connected layer is replaced with global average pooling layer. In addition to the various learning stages, different regulatory units such as batch normalization and dropout are also incorporated to optimize CNN performance [43]. The arrangement of CNN components play a fundamental role in designing new architectures and thus achieving enhanced performance. This section briefly discusses the role of these components in CNN architecture.

        Convolutional layer is composed of a set of convolutional kernels (each neuron act as a kernel). These kernels are associated with a small area of the image known as a receptive field. It works by dividing the image into small blocks (receptive fields) and convolving them with a specific set of weights (multiplying elements of the filter with the corresponding receptive field elements) [43]. Convolution operation can expressed as follows:

    Where, the input image is represented by x, y I , , xy shows spatial locality and k
l K represents the lth convolutional kernel of the kth layer. Division of image into small blocks helps in extracting locally correlated pixel values. This locally aggregated information is also known as feature motif. Different set of features within image are extracted by sliding convolutional kernel on the whole image with the same set of weights. This weight sharing feature of convolution operation makes CNN parameter efficient as compared to fully connected Nets. Convolution operation may further be categorized into different types based on the type and size of filters, type of padding, and the direction of convolution [44]. Additionally, if the kernel is symmetric, the convolution operation becomes a correlation operation [16].

        Feature motifs, which result as an output of convolution operation can occur at different locations in the image. Once features are extracted, its exact location becomes less important as long as its approximate position relative to others is preserved. Pooling or downsampling like convolution, is an interesting local operation. It sums up similar information in the neighborhood of the receptive field and outputs the dominant response within this local region [45].

Equation (2) shows the pooling operation in which l Z represents the lth output feature map, ,lxyF  shows the lth input feature map, whereas p f (.) defines the type of pooling operation. The use ofpooling operation helps to extract a combination of features, which are invariant to translational shifts and small distortions [13], [46]. Reduction in the size of feature map to invariant feature set not only regulates complexity of the network but also helps in increasing the generalization by reducing overfitting. Different types of pooling formulations such as max, average, L2, overlapping, spatial pyramid pooling, etc. are used in CNN [47]–[49].

        Activation function serves as a decision function and helps in learning a complex pattern. Selection of an appropriate activation function can accelerate the learning process. Activation function for a convolved feature map is defined in equation (3).

In above equation, k l F is an output of a convolution operation, which is assigned to activation  function; A f (.) that adds non-linearity and returns a transformed output k  l T for kth layer. In  literature, different activation functions such as sigmoid, tanh, maxout, ReLU, and variants of  ReLU such as leaky ReLU, ELU, and PReLU [39], [48], [50], [51] are used to inculcate nonlinear  combination of features. However, ReLU and its variants are preferred over others  activations as it helps in overcoming the vanishing gradient problem [52], [53].

        Batch normalization is used to address the issues related to internal covariance shift within feature maps. The internal covariance shift is a change in the distribution of hidden units’ values, which slow down the convergence (by forcing learning rate to small value) and requires careful initialization of parameters. Batch normalization for a transformed feature map k lT is shown in equation (4).

      In equation (4), k l N represents normalized feature map, kl F is the input feature map, B and 2 B   depict mean and variance of a feature map for a mini batch respectively. Batch normalization  unifies the distribution of feature map values by bringing them to zero mean and unit variance [54]. Furthermore, it smoothens the flow of gradient and acts as a regulating factor, which thus helps in improving generalization of the network.

        Dropout introduces regularization within the network, which ultimately improves generalization by randomly skipping some units or connections with a certain probability. In NNs, multiple connections that learn a non-linear relation are sometimes co-adapted, which causes overfitting [55]. This random dropping of some connections or units produces several thinned network architectures, and finally one representative network is selected with small weights. This selected architecture is then considered as an approximation of all of the proposed networks [56].

       Fully connected layer is mostly used at the end of the network for classification purpose. Unlike pooling and convolution, it is a global operation. It takes input from the previous layer and globally analyses output of all the preceding layers [57]. This makes a non-linear combination of selected features, which are used for the classification of data. [58].

       Nowadays, CNNs are considered as the most widely used algorithms among biologically inspired AI techniques. CNN history begins from the neurobiological experiments conducted by Hubel and Wiesel (1959, 1962) [14], [59]. Their work provided a platform for many cognitive models, almost all of which were latterly replaced by CNN. Over the decades, different efforts have been carried out to improve the performance of CNNs. This history is pictorially represented in Fig. 3. These improvements can be categorized into five different eras and are discussed below.

       CNNs have been applied to visual tasks since the late 1980s. In 1989, LeCuN et al. proposed the first multilayered CNN named as ConvNet, whose origin rooted in Fukushima’s Neocognitron [60], [61]. LeCuN proposed supervised training of ConvNet, using Backpropagation algorithm [7], [62] in comparison to the unsupervised reinforcement learning scheme used by its predecessor Neocognitron. LeCuN’s work thus made a foundation for the modern 2D CNNs. Supervised training in CNN provides the automatic feature learning ability from raw input, rather than designing of handcrafted features, used by traditional ML methods. This ConvNet showed successful results for handwritten digit and zip code recognition related problems [63]. In 1998, ConvNet was improved by LeCuN and used for classifying characters in a document recognition application [64]. This modified architecture was named as LeNet-5, which was an improvement over the initial CNN as it can extract feature representation in a hierarchical way from raw pixels [65]. Reliance of LeNet-5 on fewer parameters along with consideration of spatial topology of images enabled CNN to recognize rotational variants of the image [65]. Due to the good performance of CNN in optical character recognition, its commercial use in ATM and Banks started in 1993 and 1996, respectively. Though, many successful milestones were achieved by LeNet-5, yet the main concern associated with it was that its discrimination power was not scaled to classification tasks other than hand recognition.

       In the late 1990s and early 2000s, interest in NNs reduced and less attention was given to explore the role of CNNs in different applications such as object detection, video surveillance, etc. Use of CNN in ML related tasks became dormant due to the insignificant improvement in performance at the cost of high computational time. At that time, other statistical methods and, in particular, SVM became more popular than CNN due to its relatively high performance [66]–[68]. It was widely presumed in early 2000 that the backpropagation algorithm used for training of CNN was not effective in converging to optimal points and therefore unable to learn useful features in supervised fashion as compared to handcrafted features [69]. Meanwhile, different researchers kept working on CNN and tried to optimize its performance. In 2003, Simard et al. improved CNN architecture and showed good results as compared to SVM on a hand digit benchmark dataset; MNIST [64], [68], [70]–[72]. This performance improvement expedited the research in CNN by extending its application in optical character recognition (OCR) to other script’s character recognition [72]–[74], deployment in image sensors for face detection in video conferencing, and regulation of street crimes, etc. Likewise, CNN based systems were industrialized in markets for tracking customers [75]–[77]. Moreover, CNN’s potential in other applications such as medical image segmentation, anomaly detection, and robot vision was also explored [78]–[80].

       Deep NNs have generally complex architecture and time intensive training phase that sometimes spanned over weeks and even months. In early 2000, there were only a few techniques for the training of deep Networks. Additionally, it was considered that CNN is not able to scale for complex problems. These challenges halted the use of CNN in ML related tasks.

      To address these problems, in 2006 many interesting methods were reported to overcome the difficulties encountered in the training of deep CNNs and learning of invariant features. Hinton proposed greedy layer-wise pre-training approach in 2006, for deep architectures, which revived and reinstated the importance of deep learning [81], [82]. The revival of a deep learning [83], [84] was one of the factors, which brought deep CNNs into the limelight. Huang et al. (2006) used max pooling instead of subsampling, which showed good results by learning of invariant features [46], [85].                   

       Availability of big training data, hardware advancements, and computational resources contributed to advancement in CNN algorithms. Renaissance of CNN in object detection, image classification, and segmentation related tasks had been observed in this period [9], [96]. However, the success of CNN in image classification tasks was not only due to the result of aforementioned factors but largely contributed by the architectural modifications, parameter optimization, incorporation of regulatory units, and reformulation and readjustment of connections within the network [39], [42], [97].

       The main breakthrough in CNN performance was brought by AlexNet [21]. AlexNet won the 2012-ILSVRC competition, which has been one of the most difficult challenges in image detection and classification. AlexNet improved performance by exploiting depth (incorporating multiple levels of transformation) and introduced regularization term in CNN. The exemplary performance of AlexNet [21] compared to conventional ML techniques in 2012-ILSVRC (AlexNet reduced error rate from 25.8 to 16.4) suggested that the main reason of the saturation in CNN performance before 2006 was largely due to the unavailability of enough training data and computational resources. In summary, before 2006, these resource deficiencies made it hard to train a high-capacity CNN without deterioration of performance [98].             

       It is generally observed the major improvements in CNN performance occurred from 2015-2019. The research in CNN is still on going and has a significant potential of improvement. Representational capacity of CNN depends on its depth and in a sense can help in learning complex problems by defining diverse level of features ranging from simple to complex. Multiple levels of transformation make learning easy by chopping complex problems into 15 smaller modules. However, the main challenge faced by deep architectures is the problem of negative learning, which occurs due to diminishing gradient at lower layers of the network. To handle this problem, different research groups worked on readjustment of layers connections and design of new modules. In earlier 2015, Srivastava et al. used the concept of cross-channel connectivity and information gating mechanism to solve the vanishing gradient problem and to improve the network representational capacity [101]–[103]. This idea got famous in late 2015 and a similar concept of residual blocks or skip connections was coined [31]. Residual blocks are a variant of cross-channel connectivity, which smoothen learning by regularizing the flow of information across blocks [104]–[106]. This idea was used in ResNet architecture for the training of 150 layers deep network [31]. The idea of cross-channel connectivity is further extended to multilayer connectivity by Deluge, DenseNet, etc. to improve representation [107], [108].

        In the year 2016, the width of the network was also explored in connection with depth to improve feature learning [34], [35]. Apart from this, no new architectural modification became prominent but instead, different researchers used hybrid of the already proposed architectures to improve deep CNN performance [33], [104]–[106], [109], [110]. This fact gave the intuition that there might be other factors more important as compared to the appropriate assembly of the network units that can effectively regulate CNN performance. In this regard, Hu et al. (2017) identified that the network representation has a role in learning of deep CNNs [111]. Hu et al. introduced the idea of feature map exploitation and pinpointed that less informative and domain extraneous features may affect the performance of the network to a larger extent. He exploited the aforementioned idea and proposed new architecture named as Squeeze and Excitation Network (SE-Network) [111]. It exploits feature map (commonly known as channel in literature) information by designing a specialized SE-block. This block assigns weight to each feature map depending upon its contribution in class discrimination. This idea was further investigated by different researchers, which assign attention to important regions by exploiting both spatial and feature map (channel) information [37], [38], [112]. In 2018, a new idea of channel boosting was introduced by Khan et al [36]. The motivation behind the training of network with boosted channel representation was to use an enriched representation. This idea effectively boost the performance of a CNN by learning diverse features as well as exploiting the already learnt features through the concept of TL.