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)
Filters in convolutional networks are typically parameterized in a pixel basis, that does not take prior knowledge about the visual world into account. We investigate the generalized notion of frames, that can be designed with image properties in mind, as alternatives to this parametrization. We show that frame-based ResNets and Densenets can improve(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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