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So many handcrafted features.
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Traditional methods of feature extraction require handcrafted features.
One of the promises of deep learning is replacing handcrafted features with efficient algorithms for unsupervised or semi-supervised feature learning and hierarchical feature extraction [9].
Experimental results show the superiority of the learned features over alternative classifiers constructed using state-of-the-art handcrafted features.
We used handcrafted features issued from previous medical challenges MITOS @ ICPR 2012, AMIDA-13 and projects (MICO ANR TecSan) expertise.
However, the exited hash based methods for mobile visual search all try to compress the exited classical handcrafted features into binary code.
Our promising results demonstrate the excellent performance of the proposed CNNs in simultaneously detecting the centers of both the fovea and OD without human intervention or handcrafted features.
Most conventional methods for the task of relation extraction focus on designing effective handcrafted features or learning a semantic representation of the whole sentence.
We compare the proposed approach with the current state-of-the-art methods for movie classification that employ well-known image descriptors and other low-level handcrafted features.
However, as the existed hash-based methods all focus on how to compress the existed handcrafted features into binary codes, their performance is limited by the utilized features and the information loss in the compression process.
We highlight the supremacy of the proposed methods, compared both to the typical audio-based and deep-learning methods that adopt handcrafted features, and we evaluate our system in terms of classification success and run-time execution.
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