Variational Rectification Inference for Learning with Noisy Labels
arXiv:2603.17255v1 Announce Type: new Abstract: Label noise has been broadly observed in real-world datasets. To mitigate the negative impact of overfitting to label noise for deep models, effective strategies (\textit{e.g.}, re-weighting, or loss rectification) have been broadly applied in prevailing approaches, which have been generally learned under the meta-learning scenario. Despite the robustness of noise achieved by the probabilistic meta-learning models, they usually suffer from model collapse that degenerates generalization performance. In this paper, we propose variational rectification inference (VRI) to formulate the adaptive rectification for loss functions as an amortized variational inference problem and derive the evidence lower bound under the meta-learning framework. Specifically, VRI is constructed as a hierarchical Bayes by treating the rectifying vector as a latent variable, which can rectify the loss of the noisy sample with the extra randomness regularization an
arXiv:2603.17255v1 Announce Type: new Abstract: Label noise has been broadly observed in real-world datasets. To mitigate the negative impact of overfitting to label noise for deep models, effective strategies (\textit{e.g.}, re-weighting, or loss rectification) have been broadly applied in prevailing approaches, which have been generally learned under the meta-learning scenario. Despite the robustness of noise achieved by the probabilistic meta-learning models, they usually suffer from model collapse that degenerates generalization performance. In this paper, we propose variational rectification inference (VRI) to formulate the adaptive rectification for loss functions as an amortized variational inference problem and derive the evidence lower bound under the meta-learning framework. Specifically, VRI is constructed as a hierarchical Bayes by treating the rectifying vector as a latent variable, which can rectify the loss of the noisy sample with the extra randomness regularization and is, therefore, more robust to label noise. To achieve the inference of the rectifying vector, we approximate its conditional posterior with an amortization meta-network. By introducing the variational term in VRI, the conditional posterior is estimated accurately and avoids collapsing to a Dirac delta function, which can significantly improve the generalization performance. The elaborated meta-network and prior network adhere to the smoothness assumption, enabling the generation of reliable rectification vectors. Given a set of clean meta-data, VRI can be efficiently meta-learned within the bi-level optimization programming. Besides, theoretical analysis guarantees that the meta-network can be efficiently learned with our algorithm. Comprehensive comparison experiments and analyses validate its effectiveness for robust learning with noisy labels, particularly in the presence of open-set noise.
Executive Summary
This article proposes a novel approach to mitigate the negative impact of label noise in deep models, known as Variational Rectification Inference (VRI). By formulating the adaptive rectification for loss functions as an amortized variational inference problem, VRI derives an evidence lower bound under the meta-learning framework. The proposed method treats the rectifying vector as a latent variable, allowing for rectification of the loss of the noisy sample with extra randomness regularization. Through a hierarchical Bayes approach, VRI is more robust to label noise and significantly improves generalization performance. Comprehensive comparison experiments validate its effectiveness for robust learning with noisy labels, particularly in the presence of open-set noise.
Key Points
- ▸ Proposes Variational Rectification Inference (VRI) to mitigate label noise in deep models
- ▸ Formulates adaptive rectification as an amortized variational inference problem
- ▸ Derives evidence lower bound under meta-learning framework using hierarchical Bayes approach
- ▸ Introduces variational term to estimate conditional posterior accurately and avoid model collapse
Merits
Strength in Addressing Label Noise
VRI effectively addresses label noise in deep models by formulating adaptive rectification as an amortized variational inference problem, providing a robust solution for generalization performance.
Improved Generalization Performance
The proposed method significantly improves generalization performance by introducing a variational term to estimate the conditional posterior accurately and avoid model collapse.
Demerits
Limitation in Computational Complexity
The bi-level optimization programming used in VRI may increase computational complexity, potentially limiting its applicability to larger datasets or more complex models.
Assumption of Smoothness
The smoothness assumption implemented in the elaborated meta-network and prior network may not hold in all cases, potentially affecting the reliability of rectification vectors.
Expert Commentary
The proposed Variational Rectification Inference (VRI) approach demonstrates a novel and effective solution to mitigate label noise in deep models. By formulating adaptive rectification as an amortized variational inference problem, VRI provides a robust framework for handling noisy labels. The introduction of a variational term to estimate the conditional posterior accurately and avoid model collapse is a significant improvement over existing methods. However, the bi-level optimization programming used in VRI may increase computational complexity, which should be addressed in future work. Additionally, the smoothness assumption implemented in the elaborated meta-network and prior network may not hold in all cases, potentially affecting the reliability of rectification vectors. Despite these limitations, VRI is a significant contribution to the field of deep learning and label noise mitigation.
Recommendations
- ✓ Recommendation 1: Future research should focus on adapting VRI to larger datasets and more complex models to overcome computational complexity limitations.
- ✓ Recommendation 2: Investigating alternative assumptions to the smoothness assumption implemented in the elaborated meta-network and prior network is essential to ensure the reliability of rectification vectors.
