How You See Me: Understanding Convolutional Neural Networks

Gandikota, Rohit; Mishra, Deepak

doi:10.1109/tencon.2019.8929603

Cited by 3 publications

(2 citation statements)

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“…It is reflected in CNN's ability to process data in three dimensions, each of which is useful for processing voice, image, and video data. In summary, CNN consists of several layers: convolution, pooling, and fully connected [18]. In the case of image classification, the fully connected layer is the layer that provides predictions from a classification [19].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

A Comparative Analysis of Transfer Learning Architecture Performance on Convolutional Neural Network Models with Diverse Datasets

Putra,

Winanto,

Hendrowati

et al. 2023

Komputika

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Deep learning is a branch of machine learning with many highly successful applications. One application of deep learning is image classification using the Convolutional Neural Network (CNN) algorithm. Large image data is required to classify images with CNN to obtain satisfactory training results. However, this can be overcome with transfer learning architectural models, even with small image data. With transfer learning, the success rate of a model is likely to be higher. Since there are many transfer learning architecture models, it is necessary to compare each model's performance results to find the best-performing architecture. In this study, we conducted three experiments on different datasets to train models with various transfer learning architectures. We then performed a comprehensive comparative analysis for each experiment. The result is that the DenseNet-121 architecture is the best transfer learning architecture model for various datasets.

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Section: Convolutional Neural Networkmentioning

confidence: 99%

A Comparative Analysis of Transfer Learning Architecture Performance on Convolutional Neural Network Models with Diverse Datasets

Putra,

Winanto,

Hendrowati

et al. 2023

Komputika

View full text Add to dashboard Cite

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“…On the one hand, the algorithm overcomes the limitations of single‐view features of vehicle types. The self‐encoding structure and convolutional neural network (CNN; Gandikota & Mishra, 2019) are combined to construct and implement a disagreement encoding network (Ma, Wang, et al, 2020). This encoding network can automatically extract multiple sets of abstract feature views from single view data; On the other hand, it solves the problem that the vehicle classification model based on a CNN has a long training period and the cost of computing resources is high.…”

Section: Introductionmentioning

confidence: 99%

DFT: A deep feature‐based semi‐supervised collaborative training for vehicle recognition in smart cities

et al. 2021

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Intelligent transport systems (ITS) is a popular field of research in smart city. Image-based vehicle recognition is one of the most promising new techniques. The traditional appearance-based method has some limitations in feature view. The machine learning algorithm can satisfy multiple feature views, but it mainly adopts an improved supervised learning method, which needs manual annotation and has low efficiency. The semisupervised collaborative training method is widely used in the image recognition process to improve machine learning algorithms' accuracy and generalization performance. This paper proposes DFT (deep feature-based training) method for vehicle recognition in smart cities. DFT is also a semi-supervised collaborative training method on basis of two base learners. DFT adjusts data pre-processing and training process, optimizes the constructing a disagreement encoding network, and expands the recognition disagreement of pseudo-labelled samples-based training set. Compared with the typical collaborative training methods, DFT greatly accelerates the model's training process by reducing the convergence time, and improves the efficiency of vehicle recognition, while remaining the recognition accuracy unchanged.

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