Pro-t-VAE：用于鲁棒原型发现的闭式学生t混合模型
Pro-t-VAE: Closed-Form Student's t Mixtures for Robust Prototype Discovery
AI总结：
Pro-t-VAE是基于闭式学生t混合先验的深度生成框架，用于无监督图像原型发现，在视觉基准与神经成像任务上验证了其鲁棒性、准确性与效率的提升。
摘要：
我们提出了一种用于无监督图像原型发现的鲁棒深度生成框架，该框架基于重尾学生t混合先验构建，衔接了统计鲁棒性与数学可处理性。现有生成方法通常依赖易受噪声影响的高斯先验或计算密集型扩散模型，二者均无法同时提供显式潜在结构与精确解析优化。扩散模型虽能生成强先验，但迭代采样与难解似然性限制了其在原型发现中的可扩展性与可解释性。我们通过贝叶斯尺度混合公式构建学生t潜在空间来解决这些问题，这为无监督学生t混合变分自编码器生成了完全闭式的增强证据下界（ELBO），据我们所知，这是首个应用于鲁棒图像原型发现的此类目标函数，避免了先前重尾或基于扩散的框架中存在的有偏变分近似与高计算成本。将精度作为潜在变量可自动调节每个数据点的影响，对异常值进行降权以学习依赖于数据的信任度量。该模型在鲁棒性、准确性和效率方面均有所提升，具有比现有最优方法更清晰分离的潜在表示与更稳定的流形学习。我们在标准视觉基准与具有挑战性的神经成像任务上验证了该模型。在基于脑部MRI的神经退行性疾病分析中，我们的模型恢复了干净、解剖结构一致的原型，而临床噪声常使罕见亚型崩溃。这些结果凸显了我们的框架在临床环境中保留与解剖结构相关模式以实现精确发现的能力。我们的代码可在：this https URL获取。
英文摘要：
We present a robust deep generative framework for unsupervised image prototype discovery, built on a heavy-tailed Student's t mixture prior, that bridges statistical robustness and mathematical tractability. Existing generative approaches typically rely on noise-prone Gaussian priors or computationally intensive diffusion models; neither simultaneously provides explicit latent structure nor exact analytic optimization. While diffusion models can yield strong priors, iterative sampling and intractable likelihoods limit scalability and interpretability for prototype discovery. We address these issues by constructing a Student's t latent space via a Bayesian scale-mixture formulation. This yields a fully closed-form augmented ELBO for unsupervised Student's t mixture variational autoencoders, which, to our knowledge, is the first such objective applied to robust image prototype discovery, avoiding biased variational approximations and high computational costs in prior heavy-tailed or diffusion-based frameworks. Treating precision as a latent variable automatically modulates each data point's influence, downweighting outliers to learn a data-dependent measure of trust. The result is improved robustness, accuracy, and efficiency, with well-separated latent representations and more stable manifold learning than state-of-the-art methods. We validate our model on standard vision benchmarks and challenging neuroimaging tasks. In brain MRI-driven neurodegenerative disease analysis, our model recovers clean, anatomically coherent prototypes where clinical noise often collapses rare subtypes. These results highlight the ability of our framework to preserve anatomy-associated patterns for precise discovery in clinical settings. Our code is available at: https://github.com/xuzhikangnba/torch-tvae.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
