Few-Shot Class-Incremental Learning (2004.10956v2)

Abstract: The ability to incrementally learn new classes is crucial to the development of real-world artificial intelligence systems. In this paper, we focus on a challenging but practical few-shot class-incremental learning (FSCIL) problem. FSCIL requires CNN models to incrementally learn new classes from very few labelled samples, without forgetting the previously learned ones. To address this problem, we represent the knowledge using a neural gas (NG) network, which can learn and preserve the topology of the feature manifold formed by different classes. On this basis, we propose the TOpology-Preserving knowledge InCrementer (TOPIC) framework. TOPIC mitigates the forgetting of the old classes by stabilizing NG's topology and improves the representation learning for few-shot new classes by growing and adapting NG to new training samples. Comprehensive experimental results demonstrate that our proposed method significantly outperforms other state-of-the-art class-incremental learning methods on CIFAR100, miniImageNet, and CUB200 datasets.

• The paper establishes a few-shot class-incremental learning framework that prevents catastrophic forgetting using a neural gas network.
• The proposed TOPIC framework utilizes topology-preserving loss functions to maintain CNN feature stability while integrating limited new class data.
• Empirical results on CIFAR100, miniImageNet, and CUB200 show significant accuracy improvements over traditional class-incremental methods.

Few-Shot Class-Incremental Learning Analysis

The paper "Few-Shot Class-Incremental Learning" addresses a pivotal challenge in artificial intelligence: enabling Convolutional Neural Networks (CNNs) to incrementally learn additional classes from limited, labeled data without compromising the knowledge acquired from previously-learned classes. This task, termed as Few-Shot Class-Incremental Learning (FSCIL), is crucial for developing adaptive AI systems that can function effectively in dynamic environments.

The authors introduce a novel framework known as TOPIC (TOpology-Preserving knowledge InCrementer) which aims to prevent the phenomenon of catastrophic forgetting and bolster the network's ability to generalize from minimal new class data. This is achieved by utilizing a neural gas (NG) network to accurately preserve the topology of the feature manifold representing various classes. This mechanism offers a fundamental shift from traditional class-incremental learning methodologies that rely heavily on knowledge distillation and exemplar rehearsals.

Contributions and Methodology

The primary contributions of this work are threefold:

1. Establishment of the FSCIL Framework: The paper delineates FSCIL as a more challenging extension of Class-Incremental Learning (CIL), reflecting a scenario where models must incorporate new classes from a few instances while maintaining performance across all prior classes.
2. TOPIC Framework Proposal: Incorporating an NG network, TOPIC stabilizes the learned topology, reinforcing the retention of previous knowledge while facilitating adaptation for incoming data. Specifically, the neural gas compensates for forgetting by keeping the topological configuration intact during new class introductions and offers robust adaptation through a strategic growth of nodes and edges in response to new inputs.
3. Empirical Validation: Extensive empirical evaluations were conducted using CIFAR100, miniImageNet, and CUB200 datasets. TOPIC consistently demonstrated superior performance compared to state-of-the-art CIL methods, achieving considerable accuracy improvements across all datasets tested.

Technical Details

The NG network forms the backbone of the TOPIC framework, tasked with maintaining the topological configuration of the feature space. By treating it as a graph of interconnected nodes, the authors leverage competitive Hebbian learning to update centroid positions reflecting crucial changes in feature space dynamics, strengthening both the memorization of old classes and the assimilation of new ones.

The core innovation is captured through two significant loss terms within the training process:

• Anchor Loss (AL): This penalizes deviations from the established feature space topology to mitigate forgetting, grounded in the variance across dimensions.
• Min-Max Loss (MML): Devised to enhance discriminative feature learning for new classes by minimizing misclassifications through topological repositioning.

Implications and Future Prospects

This paper broadens the horizon of incremental learning by presenting tools to effectively manage few-shot scenarios where training data is scarce. The ability to incrementally and efficiently learn and stabilize knowledge in CNN models can substantially benefit AI applications involving continual learning requirements, such as autonomous systems and evolving classification tasks in dynamic settings.

As future prospects, exploring further adaptations of such topological approaches in a broader range of neural architectures may unlock new performance enhancements across domains. Additionally, bridging the algorithmic principles with neurocognitive theories could yield intriguing insights into both artificial and biological systems' learning methodologies.

In conclusion, this research paves the way for more intelligent, adaptable AI systems by innovating topological preservation techniques to address class-incremental learning deficiencies, characterizing a crucial step toward robust real-world AI deployment.