Neural Prior Estimation: Learning Class Priors from Latent Representations
arXiv:2602.17853v1 Announce Type: new Abstract: Class imbalance induces systematic bias in deep neural networks by imposing a skewed effective class prior. This work introduces the Neural Prior Estimator (NPE), a framework that learns feature-conditioned log-prior estimates from latent representations. NPE employs one or more Prior Estimation Modules trained jointly with the backbone via a one-way logistic loss. Under the Neural Collapse regime, NPE is analytically shown to recover the class log-prior up to an additive constant, providing a theoretically grounded adaptive signal without requiring explicit class counts or distribution-specific hyperparameters. The learned estimate is incorporated into logit adjustment, forming NPE-LA, a principled mechanism for bias-aware prediction. Experiments on long-tailed CIFAR and imbalanced semantic segmentation benchmarks (STARE, ADE20K) demonstrate consistent improvements, particularly for underrepresented classes. NPE thus offers a lightweigh
arXiv:2602.17853v1 Announce Type: new Abstract: Class imbalance induces systematic bias in deep neural networks by imposing a skewed effective class prior. This work introduces the Neural Prior Estimator (NPE), a framework that learns feature-conditioned log-prior estimates from latent representations. NPE employs one or more Prior Estimation Modules trained jointly with the backbone via a one-way logistic loss. Under the Neural Collapse regime, NPE is analytically shown to recover the class log-prior up to an additive constant, providing a theoretically grounded adaptive signal without requiring explicit class counts or distribution-specific hyperparameters. The learned estimate is incorporated into logit adjustment, forming NPE-LA, a principled mechanism for bias-aware prediction. Experiments on long-tailed CIFAR and imbalanced semantic segmentation benchmarks (STARE, ADE20K) demonstrate consistent improvements, particularly for underrepresented classes. NPE thus offers a lightweight and theoretically justified approach to learned prior estimation and imbalance-aware prediction.
Executive Summary
The article introduces the Neural Prior Estimator (NPE), a framework that learns class priors from latent representations in deep neural networks. NPE addresses the issue of class imbalance, which induces systematic bias in neural networks. The framework employs Prior Estimation Modules trained with the backbone network via a one-way logistic loss, allowing it to recover the class log-prior up to an additive constant. This approach offers a theoretically grounded method for adaptive signal estimation without requiring explicit class counts or distribution-specific hyperparameters. The NPE framework demonstrates consistent improvements in experiments on long-tailed CIFAR and imbalanced semantic segmentation benchmarks, particularly for underrepresented classes.
Key Points
- ▸ Introduction of the Neural Prior Estimator (NPE) framework
- ▸ NPE learns feature-conditioned log-prior estimates from latent representations
- ▸ The framework provides a theoretically grounded approach to adaptive signal estimation
Merits
Theoretically Grounded
The NPE framework is analytically shown to recover the class log-prior up to an additive constant, providing a solid theoretical foundation for the approach.
Demerits
Limited Generalizability
The framework's performance may be limited to specific scenarios, such as long-tailed CIFAR and imbalanced semantic segmentation benchmarks, and may not generalize well to other domains or tasks.
Expert Commentary
The Neural Prior Estimator framework represents a significant contribution to the field of machine learning, as it provides a theoretically grounded approach to addressing class imbalance. The framework's ability to learn feature-conditioned log-prior estimates from latent representations is a key innovation, and its performance on long-tailed CIFAR and imbalanced semantic segmentation benchmarks is impressive. However, further research is needed to fully explore the framework's limitations and potential applications, particularly in domains where class imbalance is a significant issue.
Recommendations
- ✓ Further research should be conducted to explore the NPE framework's performance on a wider range of datasets and tasks, including those with complex class relationships and high-dimensional feature spaces.
- ✓ The framework's potential applications in real-world scenarios, such as medical diagnosis and financial forecasting, should be investigated, with a focus on evaluating its performance and fairness in these contexts.
