When Does Self-supervision Improve Few-shot Learning? (1910.03560v2)

Abstract: We investigate the role of self-supervised learning (SSL) in the context of few-shot learning. Although recent research has shown the benefits of SSL on large unlabeled datasets, its utility on small datasets is relatively unexplored. We find that SSL reduces the relative error rate of few-shot meta-learners by 4%-27%, even when the datasets are small and only utilizing images within the datasets. The improvements are greater when the training set is smaller or the task is more challenging. Although the benefits of SSL may increase with larger training sets, we observe that SSL can hurt the performance when the distributions of images used for meta-learning and SSL are different. We conduct a systematic study by varying the degree of domain shift and analyzing the performance of several meta-learners on a multitude of domains. Based on this analysis we present a technique that automatically selects images for SSL from a large, generic pool of unlabeled images for a given dataset that provides further improvements.

• The paper finds self-supervision reduces few-shot learning error rates by 4-27% on small datasets and identifies conditions determining its effectiveness.
• Domain shifts between SSL and few-shot data can harm performance, but a novel image selection technique based on domain alignment can help.
• Findings suggest SSL acts as regularization in limited data settings and reveal synergistic benefits from combining self-supervised and supervised tasks.

Self-Supervision's Impact on Few-Shot Learning

The paper "When Does Self-supervision Improve Few-shot Learning?" provides an analytical examination of the role of self-supervised learning (SSL) within few-shot learning contexts. Specifically, the authors seek to quantify the circumstances under which SSL contributes to error rate reductions in few-shot learning algorithms and identify the conditions that may lead to decreased performance.

Key Insights and Methodologies

The core investigation surrounds self-supervised learning's (SSL) utility in enhancing few-shot learning, which involves training machine learning models to perform tasks with a minimal number of labeled examples. Few-shot learning is of significant interest due to its potential to alleviate the data-labeling bottleneck prevalent in fields like biology and medicine, where labeled datasets are typically sparse.

Objectives and Findings

1. Effectiveness on Small Datasets: The paper acknowledges that while SSL has demonstrated efficacy on large datasets, its impact on smaller datasets is not well understood. The authors present numerical evidence showing that SSL can reduce the relative error rate by 4%-27% in few-shot meta-learners, even with modestly sized datasets.
2. Condition Dependency: The benefits of SSL are more pronounced when datasets are smaller and tasks are more challenging. This suggests that SSL may act as a valuable regularization technique in scenarios lacking abundant labeled data.
3. Domain Shift Analysis: Crucially, the paper evaluates SSL's effectiveness in scenarios involving domain shifts—situations where the data used for SSL differ in distribution from the data used for meta-learning. The results indicate a potential detriment to performance when shifts are present, underscoring the importance of domain alignment.
4. Image Selection Technique: A novel contribution is the development of a technique that selects images for SSL from a broad, unlabeled pool, enhancing performance across various benchmarks. The method relies on a domain classifier to ensure the selected images are closely aligned with the dataset's domain, mitigating potential negative impacts of heterogeneous data sources.

Implications and Future Directions

The findings underscore SSL's potential as an auxiliary mechanism that complements traditional supervised techniques in environments characterized by limited data availability. The research suggests that SSL can act as a form of regularization, benefiting feature learning frameworks by preserving essential semantic information which may be compromised when heavily tuning models to base class distinctions.

Practically, the paper advocates for strategic SSL image selection in scenarios with mixed-domain datasets to bolster few-shot learning performance. This highlights a future research pathway focused on refining domain-selection algorithms to automate and optimize SSL image sourcing.

Theoretically, the paper expands the understanding of SSL's function within the wider deep learning landscape, proposing that the combination of self-supervised and supervised tasks can yield synergistic benefits, particularly when navigating the confines of few-shot learning. This invites further research into self-supervised task combinations, not limited to but including contrastive learning methods, to comprehensively understand their collective benefits in low-data regimes.

Overall, the paper provides a rigorous examination of SSL's role within few-shot learning frameworks, offering a valuable dataset-specific strategy and setting the stage for further exploration into synergistic learning paradigms across diverse domains.