Decoupling "when to update" from "how to update" (1706.02613v2)

Abstract: Deep learning requires data. A useful approach to obtain data is to be creative and mine data from various sources, that were created for different purposes. Unfortunately, this approach often leads to noisy labels. In this paper, we propose a meta algorithm for tackling the noisy labels problem. The key idea is to decouple "when to update" from "how to update". We demonstrate the effectiveness of our algorithm by mining data for gender classification by combining the Labeled Faces in the Wild (LFW) face recognition dataset with a textual genderizing service, which leads to a noisy dataset. While our approach is very simple to implement, it leads to state-of-the-art results. We analyze some convergence properties of the proposed algorithm.

• The paper introduces a meta algorithm that decouples the decision of when to update from how to update, enhancing performance with noisy labels.
• It provides a theoretical analysis under the perceptron model, showing efficient convergence even with label noise.
• Empirical results on a gender classification task demonstrate higher accuracy than traditional noise-robust methods.

Decoupling When to Update'' fromHow to Update''

The paper introduces a novel meta algorithm to enhance the resilience of deep learning models when encountering noisy labels, a prevalent issue due to the nature of data acquisition from diverse and non-specialized sources. This approach innovatively separates the decisions around "when" an update should be made from "how" the update should be executed. The primary mechanism suggested involves maintaining two separate predictors and performing updates only when there is a disagreement between their predictions.

Key Contributions

1. Decoupling Strategy: The algorithm's central concept is to train two models simultaneously and update them based on their disagreements rather than incorrect label agreement. This strategy helps mitigate the adverse effects of noisy labels by ensuring updates are made only when there's genuine uncertainty.
2. Theoretical Convergence: The authors provide a theoretical analysis demonstrating that their algorithm, under the perceptron model, converges efficiently even with label noise. The analysis addresses two critical questions:
   • Convergence: The algorithm's expected number of iterations approximates that of the noise-free perceptron scenario.
   • Optimality: Although initial differences between predictors influence outcomes, the algorithm empirically shows robust convergence for natural data distributions.
3. Empirical Evaluation: The authors validate their method with a gender classification task using the LFW dataset combined with a textual genderizing service. In these experiments, their method outperforms existing noise-robust models like soft/hard bootstrapping and the s-model by demonstrating that it achieves higher accuracy compared to alternatives, particularly for noisy datasets.

Implications and Future Directions

• Practical Applications: The simplicity and generality of this method make it readily applicable to existing deep learning infrastructures without significant modifications. This adaptability suggests its potential utility in various real-world applications where label reliability is questionable.
• Theoretical Foundations: Although the current analysis is grounded on linear classifiers and specific noise assumptions, there is an opportunity to extend these results to more complex frameworks, potentially involving neural network architectures and deeper theoretical inquiries involving distribution-specific assumptions for optimal convergence.
• Model Initialization: The research highlights the importance of model initialization, suggesting that even simple preprocessing with traditional algorithms before applying the disagreement update rule significantly impacts performance.
• Hybrid Models: The hybrid approach of integrating the disagreement-based update with other models (like s-model) suggests the potential for blending various noise-robust strategies into a cohesive framework, improving performance further.

The paper presents significant advancements in understanding and addressing the impact of noisy labels in deep learning systems. By coupling theoretical insights with practical evaluations, it lays a robust foundation for future research and application in the domain of AI resilience to data imperfections. Future work could focus on further expanding the theoretical groundwork, optimizing initial predictor settings, and applying the methodology to broader machine learning contexts with varied noise types.