Semantic-aware Knowledge Distillation for Few-Shot Class-Incremental Learning (2103.04059v2)

Abstract: Few-shot class incremental learning (FSCIL) portrays the problem of learning new concepts gradually, where only a few examples per concept are available to the learner. Due to the limited number of examples for training, the techniques developed for standard incremental learning cannot be applied verbatim to FSCIL. In this work, we introduce a distillation algorithm to address the problem of FSCIL and propose to make use of semantic information during training. To this end, we make use of word embeddings as semantic information which is cheap to obtain and which facilitate the distillation process. Furthermore, we propose a method based on an attention mechanism on multiple parallel embeddings of visual data to align visual and semantic vectors, which reduces issues related to catastrophic forgetting. Via experiments on MiniImageNet, CUB200, and CIFAR100 dataset, we establish new state-of-the-art results by outperforming existing approaches.

• The paper introduces a novel semantic-aware knowledge distillation method that utilizes semantic word embeddings to combat catastrophic forgetting and overfitting in few-shot class-incremental learning.
• The approach employs attention-based multiple embeddings guided by semantic representation clusters to achieve state-of-the-art performance on datasets like MiniImageNet, CIFAR100, and CUB200.
• Integrating descriptive semantics via semantic embeddings significantly enhances the ability to generalize and preserve past knowledge during incremental learning.

Semantic-aware Knowledge Distillation for Few-Shot Class-Incremental Learning

The research paper titled "Semantic-aware Knowledge Distillation for Few-Shot Class-Incremental Learning" introduces an innovative approach to tackle the complexities of few-shot class-incremental learning (FSCIL). This field aims to enable models to learn new classes incrementally with a limited number of training samples, without hindering the performance on previously learned data—a challenging scenario known for inducing catastrophic forgetting in neural networks.

Overview

The paper proposes a novel knowledge distillation method enriched with semantic understanding, addressing both catastrophic forgetting and class-overfitting challenges in FSCIL. The authors leverage semantic word embeddings, such as word2vec and GloVe, as auxiliary data to guide the distillation process. By projecting model outputs into corresponding semantic spaces, they aim to maintain knowledge of old classes while learning few new instances more effectively.

Methodology

The authors introduce a multi-faceted approach consisting of:

1. Semantic-aware Knowledge Distillation: In this framework, semantic vectors guide the distillation loss to retain relevant information from past tasks. This method aligns the model’s output with semantic word vectors, allowing new knowledge to be integrated while minimizing forgetting.
2. Attention-based Multiple Embeddings: To handle new tasks robustly, the model employs an attention mechanism across several embedding spaces. These embeddings are strategically trained using superclass information obtained from semantic representation clusters. Such modular embeddings enhance the model's flexibility and reduce overfitting risks for novel tasks.
3. Algorithm Design: An ensemble of mapping and embedding modules supplements a fixed backbone architecture. This design allows the reuse of learned semantic relationships, yielding a uniform output space for classification across task increments.

Experimental Results

The proposed methodology achieved state-of-the-art performance on benchmark datasets such as MiniImageNet, CUB200, and CIFAR100. The paper reports significant improvements over existing algorithms, illustrating:

• MiniImageNet: Achieving 39.04\% accuracy in the final session, outpacing the previous best result by more than 14\%.
• CIFAR100: The proposed method reached 34.80\% accuracy, slightly exceeding current benchmarks.
• CUB200: The approach maintained its superior performance with an accuracy of 32.96\% in the last session.

Beyond outperforming existing methods, the paper reflects the robustness of the method when applied to the Dynamic Few-Shot Learning (DFSL) problem, underscoring its adaptability across similar learning paradigms.

Implications and Future Directions

The introduction of semantic embeddings into the knowledge distillation framework represents a substantial theoretical advancement, demonstrating the advantage of integrating descriptive semantics for incremental learning. Practically, the findings suggest that semantic data can be instrumental in harnessing relationships between old and new knowledge, enhancing model generalization while preserving past insights.

As for future developments, the paper’s insights could expand to broader applications in continual and lifelong learning frameworks. Understanding how different types of semantic data (e.g., contextual embeddings) could further enhance distillation processes is another avenue deserving exploration, as well as evaluating the approach's scalability for real-world, large-scale datasets. The robustness of attention mechanisms amidst more dynamic task environments could also be a focal point for extended research.

This paper makes a compelling case for the role of semantic information in mitigating catastrophic forgetting and promoting effective learning of new tasks with limited data, proposing a sophisticated framework that could redefine methodologies in incremental learning.