A Multiple-Loss Dual-Output Convolutional Neural Network for Fashion Class Classification
| Autor: | Sanjar Ibrokhimov, Mangal Sain, Kueh Lee Hui, Uchenna Joseph Maduh, Ahmed Abdulhakim Al-Absi, Okeke Stephen |
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| Rok vydání: | 2019 |
| Předmět: |
business.industry
Computer science Feature extraction 020206 networking & telecommunications Pattern recognition 02 engineering and technology Convolutional neural network Image (mathematics) Set (abstract data type) Terminal (electronics) Pattern recognition (psychology) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Network model Block (data storage) |
| Zdroj: | 2019 21st International Conference on Advanced Communication Technology (ICACT). |
| Popis: | An improved multi-loss multi-output convolutional neural network method was deployed to extract features from a given set of disjointed data (Fashion and Color) with diverse convolutional chunks in a single network. The first convolution block extracts features from the first image dataset (Fashion) and determines the classes to which they belong. The second block is responsible for learning the information encoded in the second set of data (color), classify and append such to the features extracted from the first convolutional block. Each block possesses its loss function which makes the network a multi-loss convolutional neural network. A set of double fully connected output heads are generated at the network terminal; enabling the network to perform predictions on a combination of disjointed labels. To validate the classification ability of our network model, we conducted several experiments with different network parameters and variations of data sizes and obtained remarkable classification results of 98 and 95 on the fashion and color sets respectively. |
| Databáze: | OpenAIRE |
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