Network traffic classification is an important task in modern communications. Several approaches have been proposed to improve the performance of differentiating among applications. However, most of them are based on supervised learning where only labeled data are used. In reality, a lot of datasets are partially labeled due to many reasons and unlabeled portions of the data, which can also provide informative characteristics, are ignored. To handle this issue, we propose a semi-supervised approach based on deep learning. We deployed deep learning because of its unique nature for solving problems, and its ability to take into account both labeled and unlabeled data. Moreover, it can also integrate feature extraction and classification into a single model. To achieve these goals, we propose an approach using stacked sparse autoencoder (SSAE) accompanied by denoising and dropout techniques to improve the robustness of extracted features and prevent the over-fitting problem during the training process. The obtained results demonstrate a better performance than traditional models while keeping the whole procedure automated.
Lensless cameras are ultra-thin imaging systems that replace the lens
with a thin passive optical mask and computation. Passive mask-based
lensless cameras encode depth information in their measurements for a
certain depth range. Early works have shown that this encoded depth
can be used to perform 3D reconstruction of close-range scenes.
However, these approaches for 3D reconstructions are typically
optimization based and require strong hand-crafted priors and hundreds
of iterations to reconstruct. Moreover, the reconstructions suffer
from low resolution, noise, and artifacts. In this work, we propose
FlatNet3D—a feed-forward deep network
that can estimate both depth and intensity from a single lensless
capture. FlatNet3D is an end-to-end trainable deep network that
directly reconstructs depth and intensity from a lensless measurement
using an efficient physics-based 3D mapping stage and a fully
convolutional network. Our algorithm is fast and produces high-quality
results, which we validate using both simulated and real scenes
captured using PhlatCam.
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