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Applying the Decisiveness and Robustness Metrics to Convolutional Neural Networks
We review three recently-proposed classifier quality metrics and consider their suitability for large-scale classification challenges such as applying convolutional neural networks to the 1000-class ImageNet dataset.
Localized Compression: Applying Convolutional Neural Networks to Compressed Images
Existing CNN architectures represent images as a matrix of pixel intensities with a specified dimension; this desired dimension is achieved by downgrading or cropping.
Context Exploitation using Hierarchical Bayesian Models
We learn the patterns of co-occurrence in each context, then use these patterns as hyper-parameters for a Hierarchical Bayesian Model.
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