REVIEW 5 cited by
Subspace Regularizers for Few-Shot Class Incremental Learning
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
Few-shot class incremental learning -- the problem of updating a trained classifier to discriminate among an expanded set of classes with limited labeled data -- is a key challenge for machine learning systems deployed in non-stationary environments. Existing approaches to the problem rely on complex model architectures and training procedures that are difficult to tune and re-use. In this paper, we present an extremely simple approach that enables the use of ordinary logistic regression classifiers for few-shot incremental learning. The key to this approach is a new family of subspace regularization schemes that encourage weight vectors for new classes to lie close to the subspace spanned by the weights of existing classes. When combined with pretrained convolutional feature extractors, logistic regression models trained with subspace regularization outperform specialized, state-of-the-art approaches to few-shot incremental image classification by up to 22% on the miniImageNet dataset. Because of its simplicity, subspace regularization can be straightforwardly extended to incorporate additional background information about the new classes (including class names and descriptions specified in natural language); these further improve accuracy by up to 2%. Our results show that simple geometric regularization of class representations offers an effective tool for continual learning.
Forward citations
Cited by 5 Pith papers
-
Class Balance Matters to Active Class-Incremental Learning
A distribution-matching, class-balanced selection method (CBS) improves incremental learning from unlabeled pools, beating random and standard active learning baselines on five datasets.
-
PatchGen: Learning Soft Intra-Image Predictive Subsets for Visual Generalization
PatchGen learns a sample-dependent soft mask that selects label-predictive image patches, improving visual generalization across domain, category, and combined shifts.
-
BPG: Balancing Plasticity and Generalization for Domain Incremental Learning
BPG sizes per-domain adapters inversely to a feature-separability score and replaces hard domain selection with confidence-weighted logit fusion, setting state-of-the-art accuracy and near-zero forgetting on three dom...
-
Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need
SOYO is a lightweight trainable domain selector for parameter-isolation domain incremental learning, improving parameter selection accuracy and downstream performance on six benchmarks.
-
AnchorInv: Few-Shot Class-Incremental Learning of Physiological Signals via Representation Space Guided Inversion
AnchorInv uses feature-space anchor points to invert synthetic replay samples, improving few-shot class-incremental learning on physiological time series without storing raw data.
Discussion (0). Continue with ORCID to comment.