Transcoder比SAE更易解释神经网络特征，且可进一步优化。
Transcoders Beat Sparse Autoencoders for Interpretability
- 用输入输出映射训练的Transcoder提取特征
- 相同条件下，Transcoder特征解释性显著更强
- 跳连设计降低重建误差但不影响可解释性
稀疏自编码器（SAEs）通过将深度神经网络激活值转换为高维稀疏潜在空间并重构激活值，提取人类可读特征。Transcoders与SAEs类似，但其训练目标是根据组件输入重构网络输出。本文在相同模型与数据上对比了Transcoder与SAE提取的特征，发现Transcoder特征具有显著更高的可解释性。我们还提出跳连Transcoder，在架构中加入仿射跳跃连接，结果表明其在不损害可解释性的前提下，实现更低的重建损失。
原文摘要 · Abstract (English)
Sparse autoencoders (SAEs) extract human-interpretable features from deep neural networks by transforming their activations into a sparse, higher dimensional latent space, and then reconstructing the activations from these latents. Transcoders are similar to SAEs, but they are trained to reconstruct the output of a component of a deep network given its input. In this work, we compare the features found by transcoders and SAEs trained on the same model and data, finding that transcoder features are significantly more interpretable. We also propose skip transcoders, which add an affine skip connection to the transcoder architecture, and show that these achieve lower reconstruction loss with no effect on interpretability.
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