用后验采样验证降噪VAE的潜在表示，发现效果好不等于潜在空间可靠。
On the Shape of Latent Variables in a Denoising VAE-MoG: A Posterior Sampling-Based Study
- 用哈密顿蒙特卡洛从干净信号采样后验分布，对比编码器输出
- 模型重建准确但潜在空间分布与真实后验存在明显差异
- 适合关注生成模型可信度评估的研究者参考
本文研究了在引力波事件GW150914数据上训练的带有高斯混合先验的去噪变分自编码器（VAE-MoG）的潜在空间结构。为评估模型对底层结构的捕捉能力，我们使用哈密顿蒙特卡洛（HMC）从干净输入中抽取后验样本，并与噪声数据下编码器的输出进行比较。尽管模型能准确重构信号，统计分析显示潜在空间存在明显偏差。这表明强去噪性能并不等同于可靠的潜在表示，强调了在评估生成模型时使用后验采样验证的重要性。
原文摘要 · Abstract (English)
In this work, we explore the latent space of a denoising variational autoencoder with a mixture-of-Gaussians prior (VAE-MoG), trained on gravitational wave data from event GW150914. To evaluate how well the model captures the underlying structure, we use Hamiltonian Monte Carlo (HMC) to draw posterior samples conditioned on clean inputs, and compare them to the encoder's outputs from noisy data. Although the model reconstructs signals accurately, statistical comparisons reveal a clear mismatch in the latent space. This shows that strong denoising performance doesn't necessarily mean the latent representations are reliable highlighting the importance of using posterior-based validation when evaluating generative models.
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