CaSpeR: Latent Spectral Regularization for Continual Learning
By Emanuele Frascaroli, Riccardo Benaglia, Matteo Boschini, Luca Moschella, Cosimo Fiorini, Emanuele Rodolà, Simone Calderara. 2023
12 citations recorded by the source corpus.
While biological intelligence grows organically as new knowledge is gathered throughout life, Artificial Neural Networks forget catastrophically whenever they face a changing training data distribution. Rehearsal-based Continual Learning (CL) approaches have been established as a versatile and reliable solution to overcome this limitation; however, sudden input disruptions and memory constraints are known to alter the consistency of their predictions. We study this phenomenon by investigating the geometric characteristics of the learner's latent space and find that replayed data points of different classes increasingly mix up, interfering with classification. Hence, we propose a geometric regularizer that enforces weak requirements on the Laplacian spectrum of the latent space, promoting a partitioning behavior. Our proposal, called Continual Spectral Regularizer for Incremental Learning (CaSpeR-IL), can be easily combined with any rehearsal-based CL approach and improves the performance of SOTA methods on standard benchmarks.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
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In Artificial Intelligence
In Computer Vision and Pattern Recognition
- René Vidal
- Paris V. Giampouras
- Liangzu Peng
- Ehsan Elhamifar
- Paolo Favaro
- Shuicheng Yan
- Zhouchen Lin
- Jitendra Malik
Citations in the corpus
- The Ideal Continual Learner: An Agent That Never Forgets (2023, background): “, 2022), or combinations thereof (Yan et al., 2021; Xie et al., 2022; Wang et al., 2022b; Frascaroli et al., 2023); for surveys, see, e.”