Analysis Of CNN Architecture - 770 Words

Similarly, Perlin [12] systematically used a CNN to extract high-level information from images and videos in the form of soft biometrics. Focusing on faces and people gender recognition, this approach combines convolution techniques, evolutionary computing, and clustering methods. Basically, he proposed the optimization of a predefined CNN architecture by adjusting its weights in an unsupervised way. Initially, the CNN architecture is trained by the classical gradient-based algorithm. Extracted features are grouped by a clustering process and each group is evolved by the Differential Evolution (DE) algorithm. The weights of the CNN are then updated in an unsupervised way to represent the evolved feature vectors. The proposal of clustering†¦show more content†¦These stages represent the network from input to pooling, pooling to pooling, and pooling to output layers. Using this proposal, CNNs evolved by the GA resulted in very different architectures when compared with hand-de signed ones, but often with a better performance. However, a major disadvantage of the proposed framework is the training process, which has to be done from scratch for every evolved new architecture. Fixed-size convolutional blocks also limit the proposed method by not incorporating multi-scaling analysis, and this issue will be present in most of the direct encoded methods designed for this task. Another direct encoding scheme to design CNN architectures is proposed by Masanori et al. [14]. They created new network architectures based on genetic programming. More specifically, a Cartesian Genetic Programming (CGP) encoding scheme that represents the network structure and its connectivity. The main advantage of the proposed method is the flexibility, exploring variable-length architectures and skipping connections resulted in non-standard CNN architectures. To evaluate each architecture evolved by the CGP, they used the validation dataset performance as fitness to the evolutionary programming method. They also used predefined structural blocks called highly functional modules. Using these modules, they simulate the behavior ofShow MoreRelatedHyperspectral Image Classification1485 Words   |  6 PagesClassification is a principle technique in hyperspectral images (HSI) analysis, where a label is assigned to each pixel based on its characteristics. Applying machine learning te chniques to these datasets need special consideration, since the hyperspectral images are typically represented by features vectors of extremely high dimensions. A robust HSI classification requires a prudent combination of deep feature extractor and powerful classifier. In the last one decade, extensive classification methodsRead MoreExample Of Hyperpectral Image Classification1730 Words   |  7 Pages[12]–[17]. 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