通过让多个自编码器学相似特征，提升神经网络可解释性。
Enhancing Neural Network Interpretability with Feature-Aligned Sparse Autoencoders
- 多个自编码器并行训练时，用互特征正则化强制学习一致特征。
- 在GPT-2 Small上降低21.21%重建误差，在脑电数据上降6.67%。
- 适合关注模型可解释性、特征可追踪的研究者使用。
稀疏自编码器（SAEs）在提升神经网络激活的可解释性方面展现出潜力，但可能学习到与输入无关的特征，限制其效果。本文提出 extsc{互特征正则化}（MFR），通过鼓励并行训练的SAEs学习相似特征来改进特征学习。我们通过合成数据验证：多个SAEs学习到的特征更可能与输入特征相关。在合成数据上，MFR帮助SAEs准确恢复已知输入特征。进一步将MFR扩展至去噪脑电图（EEG）数据和重构GPT-2 Small激活的SAEs，结果表明，其可使GPT-2 Small的重建损失降低21.21%，在EEG数据上降低6.67%。结果表明，利用不同SAEs间特征的一致性，可有效提升训练性能，增强SAEs在模型可解释性中的实用性。
原文摘要 · Abstract (English)
Sparse Autoencoders (SAEs) have shown promise in improving the interpretability of neural network activations, but can learn features that are not features of the input, limiting their effectiveness. We propose \textsc{Mutual Feature Regularization} \textbf{(MFR)}, a regularization technique for improving feature learning by encouraging SAEs trained in parallel to learn similar features. We motivate \textsc{MFR} by showing that features learned by multiple SAEs are more likely to correlate with features of the input. By training on synthetic data with known features of the input, we show that \textsc{MFR} can help SAEs learn those features, as we can directly compare the features learned by the SAE with the input features for the synthetic data. We then scale \textsc{MFR} to SAEs that are trained to denoise electroencephalography (EEG) data and SAEs that are trained to reconstruct GPT-2 Small activations. We show that \textsc{MFR} can improve the reconstruction loss of SAEs by up to 21.21\% on GPT-2 Small, and 6.67\% on EEG data. Our results suggest that the similarity between features learned by different SAEs can be leveraged to improve SAE training, thereby enhancing performance and the usefulness of SAEs for model interpretability.
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