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Exploiting the potential of deep reinforcement learning for classification tasks in high-dimensional and unstructured data
This paper presents a framework for efficiently learning feature selecti...
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Laguerre-Gauss Preprocessing: Line Profiles as Image Features for Aerial Images Classification
An image preprocessing methodology based on Fourier analysis together wi...
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Invariant Integration in Deep Convolutional Feature Space
In this contribution, we show how to incorporate prior knowledge to a de...
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Exploring the Deep Feature Space of a Cell Classification Neural Network
In this paper, we present contemporary techniques for visualising the fe...
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Deep Learning for Classification Tasks on Geospatial Vector Polygons
In this paper, we evaluate the accuracy of deep learning approaches on g...
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Dense Adaptive Cascade Forest: A Densely Connected Deep Ensemble for Classification Problems
Recent research has shown that deep ensemble for forest can achieve a hu...
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Deep Feature Space: A Geometrical Perspective
One of the most prominent attributes of Neural Networks (NNs) constitutes their capability of learning to extract robust and descriptive features from high dimensional data, like images. Hence, such an ability renders their exploitation as feature extractors particularly frequent in an abundant of modern reasoning systems. Their application scope mainly includes complex cascade tasks, like multi-modal recognition and deep Reinforcement Learning (RL). However, NNs induce implicit biases that are difficult to avoid or to deal with and are not met in traditional image descriptors. Moreover, the lack of knowledge for describing the intra-layer properties – and thus their general behavior – restricts the further applicability of the extracted features. With the paper at hand, a novel way of visualizing and understanding the vector space before the NNs' output layer is presented, aiming to enlighten the deep feature vectors' properties under classification tasks. Main attention is paid to the nature of overfitting in the feature space and its adverse effect on further exploitation. We present the findings that can be derived from our model's formulation, and we evaluate them on realistic recognition scenarios, proving its prominence by improving the obtained results.
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