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Building Flyweight FLIM-based CNNs with Adaptive Decoding for Object Detection
State-of-the-art (SOTA) object detection methods have succeeded in several applications at the price of relying on heavyweight neural networks, which makes them inefficient and inviable for many applications with computational resource constraints.
Semi-supervised deep learning based on label propagation in a 2D embedded space
We present a loop in which a deep neural network (VGG-16) is trained from a set with more correctly labeled samples along iterations, created by using t-SNE to project the features of its last max-pooling layer into a 2D embedded space in which labels are propagated using the Optimum-Path Forest semi-supervised classifier.
Semi-Automatic Data Annotation guided by Feature Space Projection
Data annotation using visual inspection (supervision) of each training sample can be laborious.
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