2 research outputs found
Generative Models for Novelty Detection Applications in abnormal event and situational changedetection from data series
Novelty detection is a process for distinguishing the observations that differ in some respect
from the observations that the model is trained on. Novelty detection is one of the fundamental
requirements of a good classification or identification system since sometimes the
test data contains observations that were not known at the training time. In other words, the
novelty class is often is not presented during the training phase or not well defined.
In light of the above, one-class classifiers and generative methods can efficiently model
such problems. However, due to the unavailability of data from the novelty class, training
an end-to-end model is a challenging task itself. Therefore, detecting the Novel classes in
unsupervised and semi-supervised settings is a crucial step in such tasks.
In this thesis, we propose several methods to model the novelty detection problem in
unsupervised and semi-supervised fashion. The proposed frameworks applied to different
related applications of anomaly and outlier detection tasks. The results show the superior of
our proposed methods in compare to the baselines and state-of-the-art methods
CNN-aware binary MAP for general semantic segmentation
In this paper we introduce a novel method for general semantic segmentation that can benefit from general semantics of Convolutional Neural Network (CNN). Our segmentation proposes visually and semantically coherent image segments. We use binary encoding of CNN features to overcome the difficulty of the clustering on the high-dimensional CNN feature space. These binary codes are very robust against noise and non-semantic changes in the image. These binary encoding can be embedded into the CNN as an extra layer at the end of the network. This results in real-time segmentation. To the best of our knowledge our method is the first attempt on general semantic image segmentation using CNN. All the previous papers were limited to few number of category of the images (e.g. PASCAL VOC). Experiments show that our segmentation algorithm outperform the state-of-the-art non-semantic segmentation methods by large margin