Early-Learning Regularization Prevents Memorization of Noisy Labels (2007.00151v2)

Abstract: We propose a novel framework to perform classification via deep learning in the presence of noisy annotations. When trained on noisy labels, deep neural networks have been observed to first fit the training data with clean labels during an "early learning" phase, before eventually memorizing the examples with false labels. We prove that early learning and memorization are fundamental phenomena in high-dimensional classification tasks, even in simple linear models, and give a theoretical explanation in this setting. Motivated by these findings, we develop a new technique for noisy classification tasks, which exploits the progress of the early learning phase. In contrast with existing approaches, which use the model output during early learning to detect the examples with clean labels, and either ignore or attempt to correct the false labels, we take a different route and instead capitalize on early learning via regularization. There are two key elements to our approach. First, we leverage semi-supervised learning techniques to produce target probabilities based on the model outputs. Second, we design a regularization term that steers the model towards these targets, implicitly preventing memorization of the false labels. The resulting framework is shown to provide robustness to noisy annotations on several standard benchmarks and real-world datasets, where it achieves results comparable to the state of the art.

• The paper presents a novel early-learning regularization framework that mitigates the memorization of noisy labels in neural networks.
• It demonstrates through mathematical analysis that neural networks initially capture correct patterns before succumbing to noise-induced memorization.
• Empirical results on datasets including CIFAR-10 and Clothing1M show that the method maintains robustness and competitive accuracy despite label noise.

Early-Learning Regularization Prevents Memorization of Noisy Labels

The paper "Early-Learning Regularization Prevents Memorization of Noisy Labels" by Liu et al. presents a comprehensive exploration into the effects of label noise on the training of neural networks. It introduces a novel framework designed to enhance the robustness of deep learning models when faced with noisy annotations. This research brings a mathematical grounding and empirical demonstration to the theory that deep neural networks initially learn correct data patterns ("early learning") before memorizing incorrect labels.

Key Contributions and Methodology

1. Theoretical Analysis: The authors establish that early learning and memorization are intrinsic aspects of high-dimensional classification, including in linear settings. By analyzing softmax regression models under label noise, they prove the existence of distinct learning phases, validating that even linear models exhibit early learning before succumbing to memorization.
2. Regularization Technique: The proposed method diverges from traditional approaches that either ignore or attempt label correction. Instead, it introduces early-learning regularization (ELR) to adjust gradients early in the learning process. ELR utilizes semi-supervised learning strategies to craft target probabilities using model outputs. The regularization term ensures that the learning process remains aligned with these target probabilities, thereby minimizing the impact of incorrect labels.
3. Algorithmic Implementation: The authors demonstrate the efficacy of ELR across multiple standard benchmarks and real-world datasets. The framework achieves performance comparable to state-of-the-art methods, utilizing a combination of target estimation, weight averaging, and other advanced regularization techniques.

Numerical Results

The paper provides strong empirical support for the proposed methodology, demonstrating its robustness against varying levels of noise across different datasets such as CIFAR-10, CIFAR-100, Clothing1M, and WebVision. The results show that ELR not only prevents memorization but also maintains competitiveness with leading techniques without needing complex sample selection processes.

Implications and Future Directions

The implications of this work are both practical and theoretical:

• Practical Implications: In real-world applications, where high-quality labels are often unavailable, models leveraging ELR can deliver enhanced accuracy and reliability, providing substantial benefits, particularly in cost or resource-constricted environments.
• Theoretical Implications: This research contributes to a deeper understanding of the training dynamics of neural networks under noisy conditions. The mathematical insights and framework open new avenues for designing algorithms that exploit the early learning phase to enhance model robustness.
• Future Developments: There remains potential to extend this paper to more complex and varied network architectures, exploring how early learning theory can be optimized across different domains of artificial intelligence. Integrating ELR with other machine learning paradigms could further strengthen the adaptability and efficiency of AI systems in dealing with label noise.

In conclusion, the authors effectively deliver a compelling framework grounded in robust theoretical analysis and supported by empirical evidence. This work stands as a significant contribution to the field, offering both insights and tools for enhancing neural network training in the presence of noisy annotations.