MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels (1712.05055v2)

Abstract: Recent deep networks are capable of memorizing the entire data even when the labels are completely random. To overcome the overfitting on corrupted labels, we propose a novel technique of learning another neural network, called MentorNet, to supervise the training of the base deep networks, namely, StudentNet. During training, MentorNet provides a curriculum (sample weighting scheme) for StudentNet to focus on the sample the label of which is probably correct. Unlike the existing curriculum that is usually predefined by human experts, MentorNet learns a data-driven curriculum dynamically with StudentNet. Experimental results demonstrate that our approach can significantly improve the generalization performance of deep networks trained on corrupted training data. Notably, to the best of our knowledge, we achieve the best-published result on WebVision, a large benchmark containing 2.2 million images of real-world noisy labels. The code are at https://github.com/google/mentornet

• The paper introduces MentorNet, a framework that uses a loss-based weighting mechanism to guide curriculum learning for DNNs on corrupted labels.
• The paper derives robust objective functions grounded in classical M-estimators and provides stability proofs to ensure convergence.
• The paper demonstrates MentorNet’s efficacy by achieving lower mean squared error on CIFAR benchmarks, outperforming traditional training methods.

An Academic Essay on "MentorNet: Learning Data-Driven Curriculum for Deep Neural Networks on Corrupted Labels"

The paper, "MentorNet: Learning Data-Driven Curriculum for Deep Neural Networks on Corrupted Labels" by Lu Jiang et al., addresses an important challenge in machine learning—in particular, the training of deep neural networks (DNNs) on datasets that contain corrupted labels. The authors propose MentorNet, a neural network architecture designed to create a data-driven curriculum that guides the training process of another neural network, referred to as StudentNet.

Core Contributions

The MentorNet framework introduces a novel, automated curriculum learning paradigm, enhancing the robustness of DNNs when facing label noise. The key contributions of the paper include:

1. Loss-based Weighting Mechanism: MentorNet determines sample weights based on the loss values of samples, aiming to effectively mitigate the impact of corrupted labels during training.
2. Derivation of Objective Functions: The paper rigorously derives objective functions that guide the optimization of sample weights. The mathematical underpinnings are rooted in robust optimization techniques, reflecting the integration of classical robust M-estimators.
3. Stability Proofs: A solid theoretical foundation is provided through stability proofs, ensuring that the optimization process will converge under defined conditions.
4. Implementation of MentorNet Architectures: Various neural network architectures are explored for use in MentorNet, including Logistic Regression, MLP, CNN, and LSTM, emphasizing performance comparisons tailored to different predefined curriculums.

Key Numerical Results

To assess the efficacy of MentorNet, the researchers conducted experimental evaluations on CIFAR-10 and CIFAR-100 datasets with varying levels of label corruption. The authors present robust performances:

• Self-paced Learning: MentorNet achieved significant reductions in mean squared error (MSE) for self-paced learning, with benchmark results (1.6±0.5E-6) outperforming traditional methods by a considerable margin.
• Temporal Mixture Weighting: For complex weighting schemes like temporal mixture, the MentorNet architecture adhered to exhibit low MSE (1.2±1.1E-4), demonstrating effective handling of intricate curriculum designs.

The experimental section further provides an intuitive yet technically profound comparison of different MentorNet architectures, revealing that the bidirectional LSTM design consistently outperforms others across various curricular schemes.

Theoretical Implications

MentorNet's innovative curriculum learning paradigm aligns with both classical robust estimation techniques and modern neural network methodologies. The integration of established statistical principles ensures that the proposed method is theoretically sound and practically viable. This dual compliance fosters confidence in its application to real-world scenarios where label noise is prevalent.

The authors' derivation of the underlying robust objective functions connects deeply with robust M-estimators like the Huber loss, log-sum penalty, and Lorentzian. This theoretical linkage paves the way for future research to further explore and refine these optimization techniques for diverse and complex datasets.

Practical Implications and Future Developments

MentorNet's adaptability across different noise environments and its ability to function with minimal manual tuning hold significant practical implications. The data-driven approach of MentorNet could revolutionize training paradigms in scenarios where label acquisition is inherently noisy or expensive, such as medical diagnostics and large-scale industrial applications.

Future developments may focus on extending MentorNet's capabilities to handle dynamic and streaming data, incorporating more sophisticated noise detection mechanisms, and scaling the architecture to work seamlessly with ultra-large datasets. Additional empirical studies, covering an even broader set of noise patterns and variations in data distribution, could provide deeper insights into the robustness and flexibility of the trained models.

Conclusion

The paper's formal analysis, empirical evaluations, and robust objective derivations contribute to a nuanced understanding of how neural networks can be trained efficiently despite the presence of corrupted labels. MentorNet stands out as a sophisticated yet highly practical framework, positioned to influence both theoretical advancements and industrial applications.