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      Impact of Fully Connected Layers on Performance of Convolutional Neural Networks for Image Classification

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          Abstract

          The Convolutional Neural Networks (CNNs), in domains like computer vision, mostly reduced the need for handcrafted features due to its ability to learn the problem-specific features from the raw input data. However, the selection of dataset-specific CNN architecture, which mostly performed by either experience or expertise is a time-consuming and error-prone process. To automate the process of learning a CNN architecture, this letter attempts at finding the relationship between Fully Connected (FC) layers with some of the characteristics of the datasets. The CNN architectures, and recently data sets also, are categorized as deep, shallow, wide, etc. This letter tries to formalize these terms along with answering the following questions. (i) What is the impact of deeper/shallow architectures on the performance of the CNN w.r.t FC layers?, (ii) How the deeper/wider datasets influence the performance of CNN w.r.t FC layers?, and (iii) Which kind of architecture (deeper/ shallower) is better suitable for which kind of (deeper/ wider) datasets. To address these findings, we have performed experiments with three CNN architectures having different depths. The experiments are conducted by varying the number of FC layers. We used four widely used datasets including CIFAR-10, CIFAR-100, Tiny ImageNet, and CRCHistoPhenotypes to justify our findings in the context of the image classification problem. The source code of this research is available at \textcolor{blue}{\url{https://github.com/shabbeersh/Impact-of-FC-layers}}.

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          A systematic study of the class imbalance problem in convolutional neural networks

          In this study, we systematically investigate the impact of class imbalance on classification performance of convolutional neural networks (CNNs) and compare frequently used methods to address the issue. Class imbalance is a common problem that has been comprehensively studied in classical machine learning, yet very limited systematic research is available in the context of deep learning. In our study, we use three benchmark datasets of increasing complexity, MNIST, CIFAR-10 and ImageNet, to investigate the effects of imbalance on classification and perform an extensive comparison of several methods to address the issue: oversampling, undersampling, two-phase training, and thresholding that compensates for prior class probabilities. Our main evaluation metric is area under the receiver operating characteristic curve (ROC AUC) adjusted to multi-class tasks since overall accuracy metric is associated with notable difficulties in the context of imbalanced data. Based on results from our experiments we conclude that (i) the effect of class imbalance on classification performance is detrimental; (ii) the method of addressing class imbalance that emerged as dominant in almost all analyzed scenarios was oversampling; (iii) oversampling should be applied to the level that completely eliminates the imbalance, whereas the optimal undersampling ratio depends on the extent of imbalance; (iv) as opposed to some classical machine learning models, oversampling does not cause overfitting of CNNs; (v) thresholding should be applied to compensate for prior class probabilities when overall number of properly classified cases is of interest.
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            Object Instance Segmentation and Fine-Grained Localization Using Hypercolumns

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              Investigating Nuisances in DCNN-Based Face Recognition

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                Author and article information

                Journal
                21 January 2019
                Article
                1902.02771
                b4fb9707-2b29-4f9f-8ec5-274d92b8042d

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                Custom metadata
                This paper is under consideration at Pattern Recognition Letters
                cs.CV

                Computer vision & Pattern recognition
                Computer vision & Pattern recognition

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