Deep convolutional neural networks (DCNNs) are an influential tool for solving various problems in the machine learning and computer vision fields. In this paper, we introduce a new deep learning model called an Inception- Recurrent Convolutional Neural Network (IRCNN), which utilizes the power of an inception network combined with recurrent layers in DCNN architecture. We have empirically evaluated the recognition performance of the proposed IRCNN model using different benchmark datasets such as MNIST, CIFAR-10, CIFAR- 100, and SVHN. Experimental results show similar or higher recognition accuracy when compared to most of the popular DCNNs including the RCNN. Furthermore, we have investigated IRCNN performance against equivalent Inception Networks and Inception-Residual Networks using the CIFAR-100 dataset. We report about 3.5 , 3.47 and 2.54 improvement in classification accuracy when compared to the RCNN, equivalent Inception Networks, and Inception- Residual Networks on the augmented CIFAR- 100 dataset respectively.
Currently, most researches have been focused on improving the recognition accuracy of DCNN models. Very little research has been conducted on recurrent architectures within convolutional neural networks. The recurrent strategy is very important for context modeling from input samples. In 2015, @cite_0 proposed a RCNN structure for the first time that dealt with object recognition tasks. The architecture consists of several blocks of recurrent convolutional layers followed by a max-pooling layer. In the second to last layer of the structure global max-pooling is used, which is followed by a soft-max layer at the end. This architecture showed state-of-the-art accuracy for object classification at the time @cite_0 @cite_12 . The Long-term Recurrent Convolutional Network (LRCN) was proposed for visual recognition and description by This architecture uses a combination of two popular techniques, CNN and LSTM. The features are extracted through the CNN, and LSTM is applied to identify how features vary with respect to time. This model shows outstanding performance for visual description @cite_43 . From the above discussion, it can be concluded that DCNNs with improved architectures are showing enormous achievement when performing visual recognition tasks.
@cite_0 @cite_43 @cite_12
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Models based on deep convolutional networks have dominated recent image interpretation tasks; we investigate whether models which are also recurrent, or "temporally deep", are effective for tasks involving sequences, visual and otherwise. We develop a novel recurrent convolutional architecture suitable for large-scale visual learning which is end-to-end trainable, and demonstrate the value of these models on benchmark video recognition tasks, image description and retrieval problems, and video narration challenges. In contrast to current models which assume a fixed spatio-temporal receptive field or simple temporal averaging for sequential processing, recurrent convolutional models are "doubly deep"' in that they can be compositional in spatial and temporal "layers". Such models may have advantages when target concepts are complex and or training data are limited. Learning long-term dependencies is possible when nonlinearities are incorporated into the network state updates. Long-term RNN models are appealing in that they directly can map variable-length inputs (e.g., video frames) to variable length outputs (e.g., natural language text) and can model complex temporal dynamics; yet they can be optimized with backpropagation. Our recurrent long-term models are directly connected to modern visual convnet models and can be jointly trained to simultaneously learn temporal dynamics and convolutional perceptual representations. Our results show such models have distinct advantages over state-of-the-art models for recognition or generation which are separately defined and or optimized. The goal of the scene labeling task is to assign a class label to each pixel in an image. To ensure a good visual coherence and a high class accuracy, it is essential for a model to capture long range (pixel) label dependencies in images. In a feed-forward architecture, this can be achieved simply by considering a sufficiently large input context patch, around each pixel to be labeled. We propose an approach that consists of a recurrent convolutional neural network which allows us to consider a large input context while limiting the capacity of the model. Contrary to most standard approaches, our method does not rely on any segmentation technique nor any task-specific features. The system is trained in an end-to-end manner over raw pixels, and models complex spatial dependencies with low inference cost. As the context size increases with the built-in recurrence, the system identifies and corrects its own errors. Our approach yields state-of-the-art performance on both the Stanford Background Dataset and the SIFT Flow Dataset, while remaining very fast at test time.