Bert De Brabandere

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Recently convolutional neural networks (ConvNets) have come up as state-of-the-art classification and detection algorithms, achieving near-human performance in visual detection. However, ConvNet algorithms are typically very computation and memory intensive. In order to be able to embed ConvNet-based classification into wearable platforms and embedded(More)
In a traditional convolutional layer, the learned filters stay fixed after training. In contrast, we introduce a new framework, the Dynamic Filter Network , where filters are generated dynamically conditioned on input. We show that this architecture is a powerful one, with increased flexibility thanks to its adaptive nature, yet without an increase in the(More)
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