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Learning to Learn with Evolutionary Growth Perceptrons
Published in Sankar K. Pal, Paul P. Wang, Genetic Algorithms for Pattern Recognition, 2017
The problem that we are facing—to develop learning algorithms that can be employed to solve various real world problems—cannot be overcome with simple one-shot learning algorithms. For this matter, we propose to look at algorithms which have the capability to learn about learning. These new algorithms must be capable of shifting their built-in bias (to favor certain subclasses of problems) automatically, by utilizing past performance, to give them a wider applicability than simple one-shot learning systems. In the next section we outline one such approach, which is based on trans-dimensional learning.
Hybrid Distributed/Local Connectionist Architectures
Published in Abraham Kandel, Gideon Langholz, Lotfi A. Zadeh, Hybrid Architectures for Intelligent Systems, 2020
Learning. The perspicuity of local representations greatly simplifies the problem of realizing some desired functionality with a neural network. Effective one-shot learning schemes are possible. In contrast, distributed networks typically require prolonged, iterative learning algorithms [5, 6]. Where one-shot learning rules are employed, performance suffers [7, 8].
Intelligent Systems
Published in Puneet Kumar, Vinod Kumar Jain, Dharminder Kumar, Artificial Intelligence and Global Society, 2021
Satyajee Srivastava, Abhishek Singh, Deepak Dudeja
The main idea behind one-shot learning is to learn the class of an object using only a few data [27]. In face recognition, we want to recognize one person’s identity by providing only one image of a person’s face as an input to the system. This can be done with the help of Siamese neural networks [28].
Thermogram classification using deep siamese network for neonatal disease detection with limited data
Published in Quantitative InfraRed Thermography Journal, 2022
Accordingly, n-shot learning, specifically one-shot and few-shots, has recently gained immense popularity in the research community for analysing medical images with a limited sample size. The ability of people to learn from little data has triggered the motivation to create models that can learn from one or a few examples. In general terms, for example, one-shot learning refers to the task of each class to classify an image according to a particular class given a single (or several) training example. One or more samples from each data category are used to prepare (train) a model that can classify new images to be encountered in the future. One of the meta-learning models that has recently gained success in applying few-shot learning, especially one-shot, in various fields is Siamese neural networks (SNNs). In the SNN architecture proposed by Bromley and LeCun for the signature verification problem, identical deep convolutional neural networks (CNNs) are trained to extract feature vectors that distinguish between the instances of each data class. The outputs obtained are then used to verify the similarity of new input images [36].