Search Results for author: Mikel Menta

Found 3 papers, 3 papers with code

Class-incremental learning: survey and performance evaluation on image classification

1 code implementation28 Oct 2020 Marc Masana, Xialei Liu, Bartlomiej Twardowski, Mikel Menta, Andrew D. Bagdanov, Joost Van de Weijer

For future learning systems incremental learning is desirable, because it allows for: efficient resource usage by eliminating the need to retrain from scratch at the arrival of new data; reduced memory usage by preventing or limiting the amount of data required to be stored -- also important when privacy limitations are imposed; and learning that more closely resembles human learning.

class-incremental learning General Classification +2

Learning to adapt class-specific features across domains for semantic segmentation

1 code implementation22 Jan 2020 Mikel Menta, Adriana Romero, Joost Van de Weijer

Recent advances in unsupervised domain adaptation have shown the effectiveness of adversarial training to adapt features across domains, endowing neural networks with the capability of being tested on a target domain without requiring any training annotations in this domain.

Semantic Segmentation Translation +1

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