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16 July 2021 Continual learning: linear layer classifier concatenation using image processing transform functions
J. Orgil, S. Karungaru, K. Terada
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Proceedings Volume 11794, Fifteenth International Conference on Quality Control by Artificial Vision; 117941A (2021) https://doi.org/10.1117/12.2589077
Event: Fifteenth International Conference on Quality Control by Artificial Vision, 2021, Tokushima, Japan
Abstract
Humans and animals have the ability to continually acquire, fine-tune, and transfer knowledge and skills throughout their lifespan. This ability, is referred to as lifelong learning. In machine learning, however, algorithms are trained on available data and re-training them on new data is a major challenge due to catastrophic forgetting. Therefore, lifelong learning remains a long-standing challenge for machine learning and neural network models. In this research we aimed to solve the continual learning catastrophic forgetting issue using image processing methods. We proposed multiple methods including alpha blending, histogram equalization and a pruner. We build a universal network, using a previously trained one as a feature extractor. After that, we implemented our proposed methods. In alpha blending method, we used a technique that separate knowledge from unknown knowledge. This method shows better results than normal concatenation technique. The other method was applying histogram equalization. In this method we used two kind of techniques; one is equalizer as pruner and the other is equalizer as enhancer. Our methods got promising results.
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J. Orgil, S. Karungaru, and K. Terada "Continual learning: linear layer classifier concatenation using image processing transform functions", Proc. SPIE 11794, Fifteenth International Conference on Quality Control by Artificial Vision, 117941A (16 July 2021); https://doi.org/10.1117/12.2589077
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KEYWORDS
Data modeling
Brain
Neural networks
Data acquisition
Machine learning
Sensors
Systems modeling
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