20 research outputs found
Chosen methods of improving small object recognition with weak recognizable features
Many object detection models struggle with several problematic aspects of
small object detection including the low number of samples, lack of diversity
and low features representation. Taking into account that GANs belong to
generative models class, their initial objective is to learn to mimic any data
distribution. Using the proper GAN model would enable augmenting low precision
data increasing their amount and diversity. This solution could potentially
result in improved object detection results. Additionally, incorporating
GAN-based architecture inside deep learning model can increase accuracy of
small objects recognition. In this work the GAN-based method with augmentation
is presented to improve small object detection on VOC Pascal dataset. The
method is compared with different popular augmentation strategies like object
rotations, shifts etc. The experiments are based on FasterRCNN model
GPGPU for Difficult Black-box Problems
AbstractDifficult black-box problems arise in many scientific and industrial areas. In this paper, efficient use of a hardware accelerator to implement dedicated solvers for such problems is discussed and studied based on an example of Golomb Ruler problem. The actual solution of the problem is shown based on evolutionary and memetic algorithms accelerated on GPGPU. The presented results prove that GPGPU outperforms CPU in some memetic algorithms which can be used as a part of hybrid algorithm of finding near optimal solutions of Golomb Ruler problem. The presented research is a part of building heterogenous parallel algorithm for difficult black-box Golomb Ruler problem
Ada-QPacknet -- adaptive pruning with bit width reduction as an efficient continual learning method without forgetting
Continual Learning (CL) is a process in which there is still huge gap between
human and deep learning model efficiency. Recently, many CL algorithms were
designed. Most of them have many problems with learning in dynamic and complex
environments. In this work new architecture based approach Ada-QPacknet is
described. It incorporates the pruning for extracting the sub-network for each
task. The crucial aspect in architecture based CL methods is theirs capacity.
In presented method the size of the model is reduced by efficient linear and
nonlinear quantisation approach. The method reduces the bit-width of the
weights format. The presented results shows that hybrid 8 and 4-bit
quantisation achieves similar accuracy as floating-point sub-network on a
well-know CL scenarios. To our knowledge it is the first CL strategy which
incorporates both compression techniques pruning and quantisation for
generating task sub-networks. The presented algorithm was tested on well-known
episode combinations and compared with most popular algorithms. Results show
that proposed approach outperforms most of the CL strategies in task and class
incremental scenarios.Comment: Paper accepted at ECAI 202
Evaluation and Implementation of n-Gram-Based Algorithm for Fast Text Comparison
This paper presents a study of an n-gram-based document comparison method. The method is intended to build a large-scale plagiarism detection system. The work focuses not only on an efficiency of the text similarity extraction but also on the execution performance of the implemented algorithms. We took notice of detection performance, storage requirements and execution time of the proposed approach. The obtained results show the trade-offs between detection quality and computational requirements. The GPGPU and multi-CPU platforms were considered to implement the algorithms and to achieve good execution speed. The method consists of two main algorithms: a document's feature extraction and fast text comparison. The winnowing algorithm is used to generate a compressed representation of the analyzed documents. The authors designed and implemented a dedicated test framework for the algorithm. That allowed for the tuning, evaluation, and optimization of the parameters. Well-known metrics (e.g. precision, recall) were used to evaluate detection performance. The authors conducted the tests to determine the performance of the winnowing algorithm for obfuscated and unobfuscated texts for a different window and n-gram size. Also, a simplified version of the text comparison algorithm was proposed and evaluated to reduce the computational complexity of the text comparison process. The paper also presents GPGPU and multi-CPU implementations of the algorithms for different data structures. The implementation speed was tested for different algorithms' parameters and the size of data. The scalability of the algorithm on multi-CPU platforms was verified. The authors of the paper provide the repository of software tools and programs used to perform the conducted experiments.he appropriate fast document comparison system. Its performance is given in the paper
Experiment on Methods for Clustering and Categorization of Polish Text
The main goal of this work was to experimentally verify the methods for a challenging task of categorization and clustering Polish text. Supervised and unsupervised learning was employed respectively for the categorization and clustering. A profound examination of the employed methods was done for the custom-built corpus of Polish texts. The corpus was assembled by the authors from Internet resources. The corpus data was acquired from the news portal and, therefore, it was sorted by type by journalists according to their specialization. The presented algorithms employ Vector Space Model (VSM) and TF-IDF (Term Frequency-Inverse Document Frequency) weighing scheme. Series of experiments were conducted that revealed certain properties of algorithms and their accuracy. The accuracy of algorithms was elaborated regarding their ability to match human arrangement of the documents by the topic. For both the categorization and clustering, the authors used F-measure to assess the quality of allocation
Using simulation to calibrate real data acquisition in veterinary medicine
This paper explores the innovative use of simulation environments to enhance
data acquisition and diagnostics in veterinary medicine, focusing specifically
on gait analysis in dogs. The study harnesses the power of Blender and the
Blenderproc library to generate synthetic datasets that reflect diverse
anatomical, environmental, and behavioral conditions. The generated data,
represented in graph form and standardized for optimal analysis, is utilized to
train machine learning algorithms for identifying normal and abnormal gaits.
Two distinct datasets with varying degrees of camera angle granularity are
created to further investigate the influence of camera perspective on model
accuracy. Preliminary results suggest that this simulation-based approach holds
promise for advancing veterinary diagnostics by enabling more precise data
acquisition and more effective machine learning models. By integrating
synthetic and real-world patient data, the study lays a robust foundation for
improving overall effectiveness and efficiency in veterinary medicine