Self-supervised Knowledge Distillation for Few-shot Learning (2006.09785v2)

Abstract: Real-world contains an overwhelmingly large number of object classes, learning all of which at once is infeasible. Few shot learning is a promising learning paradigm due to its ability to learn out of order distributions quickly with only a few samples. Recent works [7, 41] show that simply learning a good feature embedding can outperform more sophisticated meta-learning and metric learning algorithms for few-shot learning. In this paper, we propose a simple approach to improve the representation capacity of deep neural networks for few-shot learning tasks. We follow a two-stage learning process: First, we train a neural network to maximize the entropy of the feature embedding, thus creating an optimal output manifold using a self-supervised auxiliary loss. In the second stage, we minimize the entropy on feature embedding by bringing self-supervised twins together, while constraining the manifold with student-teacher distillation. Our experiments show that, even in the first stage, self-supervision can outperform current state-of-the-art methods, with further gains achieved by our second stage distillation process. Our codes are available at: https://github.com/brjathu/SKD.

• The paper introduces a novel two-stage self-supervised knowledge distillation approach that enhances feature embedding diversity and inter-class discrimination.
• The Gen-0 stage leverages self-supervised auxiliary loss to expand the output manifold, while Gen-1 uses student-teacher distillation to refine class boundaries.
• Experimental results demonstrate notable improvements, achieving 67.04% accuracy on miniImageNet for 5-way 1-shot tasks compared to previous methods.

Self-supervised Knowledge Distillation for Few-shot Learning

The paper "Self-supervised Knowledge Distillation for Few-shot Learning" addresses a critical challenge in deep learning: the ability to learn from limited labeled data, a scenario encountered frequently in real-world applications across domains. The proposed methodology advocates for a novel approach to enhance few-shot learning (FSL) by leveraging self-supervised knowledge distillation. This approach offers significant improvements over existing state-of-the-art methods by focusing on enriching the representation capacity of feature embeddings rather than relying solely on complex meta-learning frameworks.

Methodology

The authors propose a two-stage learning process to optimize the performance of neural networks in FSL tasks. The first stage focuses on maximizing the entropy of feature embeddings using self-supervised learning techniques. This step helps in crafting a robust output manifold where the feature space is large and diverse, preventing overfitting.

1. Generation Zero (Gen-0): The network is trained using a self-supervised auxiliary loss, specifically designed to increase the spacing within the output manifold by predicting transformations like image rotations. This approach ensures that the variations in input space reflect meaningfully in the feature embeddings, thereby preserving intra-class diversity instead of enforcing invariance.
2. Generation One (Gen-1): This stage employs student-teacher knowledge distillation. The network trained in Gen-0 acts as the teacher. The distillation process minimizes the entropy by aligning the output of the augmented samples to that of the original samples, enhancing between-class discrimination and ensuring the model learns meaningful inter-class relationships.

Experimental Results

Experimental evaluation was conducted on four benchmark datasets: miniImageNet, tieredImageNet, CIFAR-FS, and FC100. The results demonstrated that even the initial self-supervision trained model (Gen-0) outperformed the existing state-of-the-art, including methods like Prototypical Networks, MetaOptNet, and RFS-distill. For example, on miniImageNet, Gen-1 achieved 67.04% accuracy for 5-way 1-shot learning, representing a notable improvement over previous methods. Importantly, this demonstrates the effectiveness of self-supervised learning in capturing a comprehensive feature space with fewer examples.

Implications and Future Directions

From a theoretical standpoint, this paper contributes to the ongoing dialogue about the utility of self-supervised regimes in sparse data contexts. Practically, the approach could have impactful applications across various sectors, such as in scenarios where data annotations are costly or infeasible. Furthermore, this methodology could be extended or adapted to other scarce-data domains, including language processing and time-series forecasting, encouraging broader adoption of self-supervised techniques.

Future research might explore additional self-supervised tasks beyond rotations and further refine the distillation strategy by integrating more complex data augmentations, potentially benefiting from the synergy of multi-task learning environments. Additionally, the integration with more sophisticated network architectures could yield further insights into the strengths and limitations of this approach.

In summary, this paper presents a compelling case for self-supervised knowledge distillation in the context of FSL. The dual-stage framework effectively balances representation diversity and class discrimination, suggesting new frontiers for efficient learning in data-scarce environments.