- The paper presents LogitNorm, a technique that normalizes logit vectors to reduce undue confidence in neural network predictions.
- It adapts cross-entropy loss by enforcing a constant logit norm, preventing overconfident outputs on out-of-distribution samples.
- Extensive experiments show a decrease in false positive rates, improving OOD detection from 50.33% to 8.03% on benchmark datasets.
Mitigating Neural Network Overconfidence with Logit Normalization
The paper by Hongxin Wei et al. introduces a novel method called Logit Normalization (LogitNorm) to address the persistent problem of overconfidence in neural networks, particularly in out-of-distribution (OOD) detection scenarios. OOD detection is a critical task in machine learning, ensuring the safe deployment of models by identifying inputs that diverge from the training distribution. Neural networks often demonstrate high confidence on OOD samples, raising significant concerns about reliability and robustness.
Key Contributions
The authors propose LogitNorm as a modification to the conventional cross-entropy loss, which is predominantly used for training classifiers. This approach stems from the observation that the logit vector (pre-softmax output) norm tends to increase during training, leading to overly confident predictions, regardless of whether inputs are in-distribution (ID) or OOD. LogitNorm ensures a constant norm of the logit vector, effectively decoupling the influence of its magnitude from the training process. This normalization results in more meaningful confidence scores that better distinguish between ID and OOD inputs.
Methodology
The main technical insight is that overconfidence arises from the scaling of the logit norms, which cross-entropy loss unconsciously encourages. By enforcing a unit norm, LogitNorm hinders the softmax from assigning extreme probabilities unjustifiably, especially for OOD samples, promoting conservative predictions. The method involves dividing each logit by its L2 norm and a temperature parameter, maintaining the beneficial properties of traditional cross-entropy objective while regularizing the outputs.
Experimental Results
Extensive experiments demonstrate that LogitNorm outperforms cross-entropy loss significantly on benchmark OOD detection datasets. For example, on CIFAR-10 models using SVHN as the OOD dataset, LogitNorm reduces the false positive rate (FPR95) from 50.33% to 8.03%, reflecting a notable improvement. Moreover, experiments show that LogitNorm enhances not just the raw softmax scores but also integrates well with other scoring functions like ODIN, energy-based and gradient-norm-based methods, leading to superior OOD detection performance.
Implications and Future Work
Practically, the implementation of LogitNorm is straightforward and can be seamlessly integrated into existing architectures with minimal computation overhead, making it attractive for real-world applications of neural networks where reliability is crucial. Theoretically, this work opens new avenues to re-evaluate the loss function design in deep learning, especially concerning robustness and uncertainty quantification.
In the future, a deeper theoretical understanding could provide insights into how LogitNorm impacts the neural network's feature space and decision boundaries. Additionally, investigating its synergies with other regularization methods could further boost neural networks' performance under distributional shifts.
Overall, this paper makes significant strides in tackling a challenging problem within the scope of machine learning's deployment safety, contributing both compelling empirical evidence and a practical solution for overconfidence mitigation.