FeNeC: Enhancing Continual Learning via Feature Clustering with Neighbor- or Logit-Based Classification

• URL: http://arxiv.org/abs/2503.14301v2
• Date: Mon, 14 Apr 2025 09:20:57 GMT
• Title: FeNeC: Enhancing Continual Learning via Feature Clustering with Neighbor- or Logit-Based Classification
• Authors: Kamil Książek, Hubert Jastrzębski, Bartosz Trojan, Krzysztof Pniaczek, Michał Karp, Jacek Tabor,
• Abstract summary: We introduce FeNeC (Feature Neighborhood) and FeNeC-Log, incorporating its variant based on the log-likelihood function.<n>Our approach generalizes the existing concept by clustering to capture greater intra-class variability.<n>We demonstrate that two FeNeC variants achieve competitive performance in scenarios where task identities are unknown.
• Score: 6.720605329045581
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The ability of deep learning models to learn continuously is essential for adapting to new data categories and evolving data distributions. In recent years, approaches leveraging frozen feature extractors after an initial learning phase have been extensively studied. Many of these methods estimate per-class covariance matrices and prototypes based on backbone-derived feature representations. Within this paradigm, we introduce FeNeC (Feature Neighborhood Classifier) and FeNeC-Log, its variant based on the log-likelihood function. Our approach generalizes the existing concept by incorporating data clustering to capture greater intra-class variability. Utilizing the Mahalanobis distance, our models classify samples either through a nearest neighbor approach or trainable logit values assigned to consecutive classes. Our proposition may be reduced to the existing approaches in a special case while extending them with the ability of more flexible adaptation to data. We demonstrate that two FeNeC variants achieve competitive performance in scenarios where task identities are unknown and establish state-of-the-art results on several benchmarks.

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