19 June 2017 Training strategy for convolutional neural networks in pedestrian gender classification
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Proceedings Volume 10443, Second International Workshop on Pattern Recognition; 104431A (2017) https://doi.org/10.1117/12.2280487
Event: Second International Workshop on Pattern Recognition, 2017, Singapore, Singapore
Abstract
In this work, we studied a strategy for training a convolutional neural network in pedestrian gender classification with limited amount of labeled training data. Unsupervised learning by k-means clustering on pedestrian images was used to learn the filters to initialize the first layer of the network. As a form of pre-training, supervised learning for the related task of pedestrian classification was performed. Finally, the network was fine-tuned for gender classification. We found that this strategy improved the network’s generalization ability in gender classification, achieving better test results when compared to random weights initialization and slightly more beneficial than merely initializing the first layer filters by unsupervised learning. This shows that unsupervised learning followed by pre-training with pedestrian images is an effective strategy to learn useful features for pedestrian gender classification.
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Choon-Boon Ng, Yong-Haur Tay, Bok-Min Goi, "Training strategy for convolutional neural networks in pedestrian gender classification", Proc. SPIE 10443, Second International Workshop on Pattern Recognition, 104431A (19 June 2017); doi: 10.1117/12.2280487; https://doi.org/10.1117/12.2280487
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