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Analysis of Learned Features and Framework for Potato Disease Detection
For applications like plant disease detection, usually, a model is trained on publicly available data and tested on field data.
Learning optimally separated class-specific subspace representations using convolutional autoencoder
In this work, we propose a novel convolutional autoencoder based architecture to generate subspace specific feature representations that are best suited for classification task.
ChartNet: Visual Reasoning over Statistical Charts using MAC-Networks
To this end, we formulate the problem of reasoning over statistical charts as a classification task using MAC-Networks to give answers from a predefined vocabulary of generic answers.
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