Discriminative Learning of Imaginary Data for Few-Shot Classification

2021 ◽  
Author(s):  
Xu Zhang ◽  
Youjia Zhang ◽  
Zuyu Zhang ◽  
Jinzhuo Liu
Keyword(s):  
Discriminative Learning ◽  
Shot Classification

Two-dimensional discriminative learning in the pigeon.

1968 ◽  
Vol 65 (3, Pt.1) ◽  
pp. 427-432 ◽  
Author(s):  
R. C. Gonzalez ◽  
M. E. Bitterman
Keyword(s):  
Discriminative Learning ◽  
Two Dimensional

Actual issues of systematization of scientific knowledge on the conditions of shot in the forensic ballistics

2020 ◽  
Vol 2020 (3) ◽  
pp. 164-169
Author(s):  
Igor' Latyshov ◽  
Fedor Samuylenko
Keyword(s):  
Scientific Knowledge ◽  
Communication Systems ◽  
Production Method ◽  
Natural Phenomenon ◽  
Projectile Energy ◽  
Energy Characteristics ◽  
Shot Classification ◽  
Forensic Ballistics

In this research, there was considered a challenge of constructing a system of scientific knowledge of the shot conditions in judicial ballistics. It was observed that there are underlying factors that are intended to ensureits [scientific knowledge] consistency: identification of the list of shot conditions, which require consideration when solving expert-level research tasks on weapons, cartridges and traces of their action; determination of the communication systems in the course of objects’ interaction, which present the result of exposure to the conditions of the shot; classification of the shot conditions based on the grounds significant for solving scientific and practical problems. The article contains the characteristics of a constructive, functional factor (condition) of weapons and cartridges influence, environmental and fire factors, the structure of the target and its physical properties, situational and spatial factors, and projectile energy characteristics. Highlighted are the forms of connections formed in the course of objects’ interaction, proposed are the author’s classifications of forensically significant shooting conditions with them being divided on the basis of the following criteria: production from the object of interaction, production from a natural phenomenon, production method, results weapon operation and utilization, duration of exposure, type of structural connections between interaction objects, number of conditions that apply when firing and the forming traces.

scholarly journals Meta Learning for Few-Shot One-Class Classification

AI ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 195-208
Author(s):  
Gabriel Dahia ◽  
Maurício Pamplona Segundo
Keyword(s):  
Feature Representation ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Target Class ◽  
Feature Representations ◽  
Shot Classification ◽  
Training Stage ◽  
Comparable Performance ◽  
Meta Learning ◽  
One Class Classification

We propose a method that can perform one-class classification given only a small number of examples from the target class and none from the others. We formulate the learning of meaningful features for one-class classification as a meta-learning problem in which the meta-training stage repeatedly simulates one-class classification, using the classification loss of the chosen algorithm to learn a feature representation. To learn these representations, we require only multiclass data from similar tasks. We show how the Support Vector Data Description method can be used with our method, and also propose a simpler variant based on Prototypical Networks that obtains comparable performance, indicating that learning feature representations directly from data may be more important than which one-class algorithm we choose. We validate our approach by adapting few-shot classification datasets to the few-shot one-class classification scenario, obtaining similar results to the state-of-the-art of traditional one-class classification, and that improves upon that of one-class classification baselines employed in the few-shot setting.

scholarly journals Few-Shot Classification in Unseen Domains by Episodic Meta-Learning Across Visual Domains

2021 ◽  
Author(s):  
Yuan-Chia Cheng ◽  
Ci-Siang Lin ◽  
Fu-En Yang ◽  
Yu-Chiang Frank Wang
Keyword(s):  
Shot Classification ◽  
Meta Learning

scholarly journals Discriminative Learning Approach Based on Flexible Mixture Model for Medical Data Categorization and Recognition

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2450
Author(s):  
Fahd Alharithi ◽  
Ahmed Almulihi ◽  
Sami Bourouis ◽  
Roobaea Alroobaea ◽  
Nizar Bouguila
Keyword(s):  
Mixture Model ◽  
Hybrid Approach ◽  
Medical Data ◽  
Support Vector ◽  
Discriminative Learning ◽  
Learning Approach ◽  
X Rays ◽  
Intrinsic Nature ◽  
Discriminative Models ◽  
Dirichlet Mixture

In this paper, we propose a novel hybrid discriminative learning approach based on shifted-scaled Dirichlet mixture model (SSDMM) and Support Vector Machines (SVMs) to address some challenging problems of medical data categorization and recognition. The main goal is to capture accurately the intrinsic nature of biomedical images by considering the desirable properties of both generative and discriminative models. To achieve this objective, we propose to derive new data-based SVM kernels generated from the developed mixture model SSDMM. The proposed approach includes the following steps: the extraction of robust local descriptors, the learning of the developed mixture model via the expectation–maximization (EM) algorithm, and finally the building of three SVM kernels for data categorization and classification. The potential of the implemented framework is illustrated through two challenging problems that concern the categorization of retinal images into normal or diabetic cases and the recognition of lung diseases in chest X-rays (CXR) images. The obtained results demonstrate the merits of our hybrid approach as compared to other methods.

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