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Communication Dans Un Congrès Année : 2022

Streaming learning with Move-to-Data approach for image classification

Résumé

In Deep Neural Network training, the availability of a large amount of representative training data is the sine qua non-condition for a good generalization capacity of the model. In many real-world applications, data is not available at a glance, but coming on the fly. If a pre-trained model is fine-tuned on the new data, then catastrophic forgetting happens mostly. Incremental learning mechanisms propose ways to overcome catastrophic forgetting. Streaming learning is a type of incremental learning where models learn from new data instances as soon as they become available in a single training pass. In this work, we conduct an experimental study, on a large dataset, of an incremental/streaming learning method Move-to-Data we previously proposed, and propose an updated approach by ”re-targeting” with gradient descent which is faster than the popular streaming learning method ExStream. The method achieves better performances and computational efficiency compared to ExStream. Move-to-Data with gradient is on average 3.5 times faster than ExStream and has a similar accuracy, with 0.5% improvement compared to ExStream.
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Dates et versions

hal-04087957 , version 1 (03-05-2023)

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Citer

Abel Kahsay Gebreslassie, Jenny Benois-Pineau, Akka Zemmari. Streaming learning with Move-to-Data approach for image classification. CBMI 2022: International Conference on Content-based Multimedia Indexing, Sep 2022, Graz Austria, France. pp.167-173, ⟨10.1145/3549555.3549590⟩. ⟨hal-04087957⟩

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