Aprendizaje Incremental para la clasificación de objetos en un mundo real y dinámico
Classifiers have significantly increased their performance with the advent of deep learning methods. However, these classifiers require a large number of examples to operate, tend to be fragile in the presence of out-of-distribution examples, and are static in the sense that they are unable to learn...
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| Tipo de recurso: | tesis de maestría |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2024 |
| País: | México |
| Institución: | Instituto Nacional de Astrofísica, Óptica y Electrónica |
| Repositorio: | Repositorio Institucional del INAOE |
| Idioma: | español |
| OAI Identifier: | oai:inaoe.repositorioinstitucional.mx:1009/2529 |
| Acceso en línea: | http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/2529 |
| Access Level: | acceso abierto |
| Palabra clave: | info:eu-repo/classification/Incremental Learning/Incremental Learning info:eu-repo/classification/Machine Learning/Machine Learning info:eu-repo/classification/Convolutional Networks/Convolutional Networks info:eu-repo/classification/Hierarchical Tree/Hierarchical Tree info:eu-repo/classification/Classifier/Classifier info:eu-repo/classification/Imagery/Imagery info:eu-repo/classification/Object Classification/Object Classification info:eu-repo/classification/Support vector machine/Support vector machine info:eu-repo/classification/Open World/Open World info:eu-repo/classification/cti/7 info:eu-repo/classification/cti/33 info:eu-repo/classification/cti/3399 |
| Sumario: | Classifiers have significantly increased their performance with the advent of deep learning methods. However, these classifiers require a large number of examples to operate, tend to be fragile in the presence of out-of-distribution examples, and are static in the sense that they are unable to learn to classify examples of new classes. Incremental learning has been developed as a way to gradually increase the number of classes supported by a classifier, without degrading its performance on previously learned classes. However, they still require a large number of examples to train to know when new classes are given to the classifier. In this thesis, we propose a new incremental learning classifier that can be trained with a single unlabeled example, does not need to know whether the example comes from an existing or known class, and is able to automatically obtain new instances and the name of the example class. Specifically, the proposed system uses a segmentation algorithm to obtain information of possible objects from an initially unknown environment, automatically obtains training examples and the class name of the segmented objects from the Internet, and builds an incremental classifier, using a hierarchical k-means algorithm and support vector machines. It is shown that the proposed system is able to operate under these open-world conditions with competitive performance under open-world conditions with competitive performance. |
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