Improving Calibration of Overparameterized Neural Networks by Decoupling Feature Extraction and Classification Layers

Core Concepts

Decoupling the training of feature extraction layers and classification layers in overparameterized deep neural network architectures significantly improves model calibration while retaining accuracy.

Abstract

The paper presents two methods, Two-Stage Training (TST) and Variational Two-Stage Training (V-TST), to improve the calibration of overparameterized deep neural networks (DNNs) for image classification tasks.
Key highlights:

Jointly training feature extraction layers (e.g., convolutional or attention layers) and classification layers (fully connected layers) in DNNs can lead to poorly calibrated models.
TST first trains the DNN end-to-end using cross-entropy loss, then freezes the feature extraction layers and re-trains only the classification layers.
V-TST further improves calibration by placing a Gaussian prior on the last hidden layer outputs and training the classification layers variationally using the evidence lower-bound (ELBO) objective.
Experiments on CIFAR10, CIFAR100, and SVHN datasets show that TST and V-TST significantly improve calibration metrics like Expected Calibration Error (ECE) and Maximum Calibration Error (MCE) compared to the baseline DNN models, while maintaining similar accuracy.
The improvements hold for both convolutional (Wide Residual Networks) and transformer-based (Vision Transformers) architectures.
An ablation study suggests that the two-stage training approach, rather than just the modified architecture, is the key driver of the calibration improvements.

Stats

"If a perfectly calibrated model assigns probability p of class C in A events, we would expect p of those A events to be class C."
"Over-parameterized models under cross-entropy training tend to be overconfident."

Quotes

"Decoupling the training of feature extraction layers and classification layers in over-parametrized DNN architectures such as Wide Residual Networks (WRN) and Visual Transformers (ViT) significantly improves model calibration whilst retaining accuracy, and at a low training cost."
"Placing a Gaussian prior on the last hidden layer outputs of a DNN, and training the model variationally in the classification training stage, even further improves calibration."

Key Insights Distilled From

Decoupling Feature Extraction and Classification Layers for Calibrated Neural Networks

by Mikkel Jorda... at arxiv.org 05-03-2024

https://arxiv.org/pdf/2405.01196.pdf

Deeper Inquiries

How would the proposed methods perform on other types of overparameterized models beyond image classification, such as language models or reinforcement learning agents

The proposed methods of decoupling feature extraction and classification layers for calibrated neural networks could potentially be applied to other types of overparameterized models beyond image classification, such as language models or reinforcement learning agents. In the case of language models, the feature extraction layers could correspond to the embedding layers and the classification layers could be the output layers responsible for predicting the next word in a sequence. By decoupling the training of these layers, similar improvements in calibration could be achieved while maintaining model accuracy. For reinforcement learning agents, the feature extraction layers could represent the state representation and the classification layers could correspond to the action selection mechanism. By training these components separately, the agent's decision-making process could be better calibrated, leading to more reliable and accurate actions in different environments.

What are the potential drawbacks or limitations of the two-stage training approach, and how could they be addressed

One potential drawback of the two-stage training approach is the increased computational cost and training time associated with training the model in two separate stages. This could be addressed by optimizing the training process to make it more efficient, such as by using parallel processing or distributed training techniques. Another limitation could be the need for hyperparameter tuning to determine the optimal architecture and dimensions for the classification layers in Stage 2. This could be addressed by automating the hyperparameter search process using techniques like Bayesian optimization or reinforcement learning. Additionally, the two-stage training approach may not always lead to improvements in model performance, especially if the model is not overparameterized or if the feature extraction and classification layers are already well-aligned. To address this, it is important to carefully evaluate the model's performance and calibration metrics before and after applying the two-stage training approach.

Could the insights from this work be extended to develop new techniques for improving model robustness and out-of-distribution generalization, beyond just calibration

The insights from this work could be extended to develop new techniques for improving model robustness and out-of-distribution generalization beyond just calibration. For example, by incorporating uncertainty estimation methods such as Bayesian neural networks or dropout regularization, models could better quantify their uncertainty and make more informed decisions in uncertain or out-of-distribution scenarios. Additionally, techniques like data augmentation, mix-up training, or label smoothing could be combined with the two-stage training approach to enhance model generalization and robustness to unseen data. By exploring these avenues, researchers could develop more reliable and trustworthy machine learning models that perform well in a variety of real-world settings.