35 research outputs found
Feature Representation for Online Signature Verification
Biometrics systems have been used in a wide range of applications and have
improved people authentication. Signature verification is one of the most
common biometric methods with techniques that employ various specifications of
a signature. Recently, deep learning has achieved great success in many fields,
such as image, sounds and text processing. In this paper, deep learning method
has been used for feature extraction and feature selection.Comment: 10 pages, 10 figures, Submitted to IEEE Transactions on Information
Forensics and Securit
MobileDenseNet: A new approach to object detection on mobile devices
Object detection problem solving has developed greatly within the past few
years. There is a need for lighter models in instances where hardware
limitations exist, as well as a demand for models to be tailored to mobile
devices. In this article, we will assess the methods used when creating
algorithms that address these issues. The main goal of this article is to
increase accuracy in state-of-the-art algorithms while maintaining speed and
real-time efficiency. The most significant issues in one-stage object detection
pertains to small objects and inaccurate localization. As a solution, we
created a new network by the name of MobileDenseNet suitable for embedded
systems. We also developed a light neck FCPNLite for mobile devices that will
aid with the detection of small objects. Our research revealed that very few
papers cited necks in embedded systems. What differentiates our network from
others is our use of concatenation features. A small yet significant change to
the head of the network amplified accuracy without increasing speed or limiting
parameters. In short, our focus on the challenging CoCo and Pascal VOC datasets
were 24.8 and 76.8 in percentage terms respectively - a rate higher than that
recorded by other state-of-the-art systems thus far. Our network is able to
increase accuracy while maintaining real-time efficiency on mobile devices. We
calculated operational speed on Pixel 3 (Snapdragon 845) to 22.8 fps. The
source code of this research is available on
https://github.com/hajizadeh/MobileDenseNet
Class-Adaptive Sampling Policy for Efficient Continual Learning
Continual learning (CL) aims to acquire new knowledge while preserving
information from previous experiences without forgetting. Though buffer-based
methods (i.e., retaining samples from previous tasks) have achieved acceptable
performance, determining how to allocate the buffer remains a critical
challenge. Most recent research focuses on refining these methods but often
fails to sufficiently consider the varying influence of samples on the learning
process, and frequently overlooks the complexity of the classes/concepts being
learned. Generally, these methods do not directly take into account the
contribution of individual classes. However, our investigation indicates that
more challenging classes necessitate preserving a larger number of samples
compared to less challenging ones. To address this issue, we propose a novel
method and policy named 'Class-Adaptive Sampling Policy' (CASP), which
dynamically allocates storage space within the buffer. By utilizing concepts of
class contribution and difficulty, CASP adaptively manages buffer space,
allowing certain classes to occupy a larger portion of the buffer while
reducing storage for others. This approach significantly improves the
efficiency of knowledge retention and utilization. CASP provides a versatile
solution to boost the performance and efficiency of CL. It meets the demand for
dynamic buffer allocation, accommodating the varying contributions of different
classes and their learning complexities over time
Expanding Explainability Horizons: A Unified Concept-Based System for Local, Global, and Misclassification Explanations
Explainability of intelligent models has been garnering increasing attention
in recent years. Of the various explainability approaches, concept-based
techniques are notable for utilizing a set of human-meaningful concepts instead
of focusing on individual pixels. However, there is a scarcity of methods that
consistently provide both local and global explanations. Moreover, most of the
methods have no offer to explain misclassification cases. To address these
challenges, our study follows a straightforward yet effective approach. We
propose a unified concept-based system, which inputs a number of
super-pixelated images into the networks, allowing them to learn better
representations of the target's objects as well as the target's concepts. This
method automatically learns, scores, and extracts local and global concepts.
Our experiments revealed that, in addition to enhancing performance, the models
could provide deeper insights into predictions and elucidate false
classifications