| Autor: |
Dražić, Ante, Kuzmanić Skelin, Ana, Bonković, Mirjana |
| Jazyk: |
angličtina |
| Rok vydání: |
2019 |
| Předmět: |
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| Popis: |
Recent advances in architectures of deep learning models have improved their performance in the field of computer vision with an important contribution to image classification. Understanding performance characteristics of deep learning on these high dimensional datasets are still elusive due to the large number of parameters leading to complex interactions and vagueness in the understanding of an underlying decision processes. To this end, visual representation of learned image features and the decision dataflow enables insights into multi modular structure. In this work we explore key aspects of the convolutional neural network (CNN) as the most studied network architecture of deep learning image classifiers through data and process visualization graphs in a TensorFlow framework on a CIFAR-10 dataset. Comparative performance between three different improvement techniques is given which is presented in such a way as to demonstrate the usability of graphical presentation as a tool for visualizing computational process. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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