一些经典的论文

VAEs

• Auto-Encoding Variational Bayes

  提出VAE

• Wasserstein Auto-Encoders

  提出WAE

• ICLR20 - FROM VARIATIONAL TO DETERMINISTIC AUTOENCODERS

  Github: Link

  解决的问题: 建模数据的隐空间分布模型，提出RAE（Regularized Autoencoder）。

  创新与独特: RAE无需假设数据的隐空间分布符合任何先验分布。

  采用的方法

  1. 将传统VAE中，从先验分布中采集随机数据送入decoder的操作，视为一种decoder的正则项，用于辅助学习一个平滑的分布。
  2. 将数据的隐空间分布视为拥有某均值与固定方差的一个未知分布，得到数据隐空间表达后，再估计一个后验的数据分布。

  为何选择/如何应用: 本文的方法作为一种强大的数据分布估计方法，不预设任何先验分布，在其他复杂的异常检测任务上能够对数据进行建模。

  数据集

  1. MNIST
  2. CIFAR
  3. CELEBA（人像属性数据集）

• NIPS18 - Gaussian Process Prior Variational Autoencoders

  提出GPVAE，即隐空间变量遵从高斯过程，而非一个时不变的分布。

• ICLR19 - INTERPRETABLE DISCRETE REPRESENTATION LEARNING ON TIME SERIES

  提出SOM-VAE, Github

  High-dimensional time series are common in many domains. Since human cognition is
  not optimized to work well in high-dimensional spaces, these areas could benefit from
  interpretable low-dimensional representations. However, most representation learning
  algorithms for time series data are difficult to interpret. This is due to non-intuitive mappings
  from data features to salient properties of the representation and non-smoothness over time.
  To address this problem, we propose a new representation learning framework building on
  ideas from interpretable discrete dimensionality reduction and deep generative modeling.
  This framework allows us to learn discrete representations of time series, which give rise to
  smooth and interpretable embeddings with superior clustering performance. We introduce
  a new way to overcome the non-differentiability in discrete representation learning and
  present a gradient-based version of the traditional self-organizing map algorithm that is
  more performant than the original. Furthermore, to allow for a probabilistic interpretation of
  our method, we integrate a Markov model in the representation space. This model uncovers
  the temporal transition structure, improves clustering performance even further and provides
  additional explanatory insights as well as a natural representation of uncertainty.
  We evaluate our model in terms of clustering performance and interpretability on static
  (Fashion-)MNIST data, a time series of linearly interpolated (Fashion-)MNIST images, a
  chaotic Lorenz attractor system with two macro states, as well as on a challenging real world
  medical time series application on the eICU data set. Our learned representations compare
  favorably with competitor methods and facilitate downstream tasks on the real world data.

  …

  Time series from the data space [green] are encoded
  by a neural network [black] time-point-wise into the latent space. The latent data manifold is approximated
  with a self-organizing map (SOM) [red]. In order to achieve a discrete representation, every latent data point
  ($z_e$) is mapped to its closest node in the SOM ($z_q$). A Markov transition model [blue] is learned to predict
  the next discrete representation ($z_q ^{t+1}$) given the current one ($z_q ^{t}$). The discrete representations can then be
  decoded by another neural network back into the original data space.

• NIPS17 - Neural Discrete Representation Learning

  Github: Link

  A useful blog: Link

  Some code and its explaination: Link

  解决的问题: 学习数据的隐空间离散化表示，该离散化表示可以用于压缩编码、生成更清晰的图像等。

  创新与独特：第一个学习数据离散表示的工作。

  采用的方法

  1. 首先将数据使用encoder映射到隐空间，然后学习数据在隐空间的类别分布(categorical distribution)，
     然后使用一个编码表对连续的隐空间表示做近似，得到离散化的表示，该离散化的表示载送入decoder进行数据还原（文章主要着笔与图像的生成，文章中使用pixelCNN作为decoder，该结构接收离散化的表示作为输入。文章也介绍了音频的生成，即序列生成。）。

  2. 进行隐空间离散化的主要方式是，将一个D维的向量通过查表操作，量化到距离他最近的一个数值上。而该表也将在训练中更新，使其能够更加接近真实值。

  3. 本文的大量笔墨用在如何对这个网络进行更新上，因为查表的量化操作是无法进行梯度计算的。

  为何选择/如何应用

  1. 本文所提出的方法能够更逼真的重建样本，媲美BigGAN.

  2. 所提出的方法适用于建模离散数据，而大多数的真实世界数据均为离散化的。

  数据集

  1. CIFAR10, ImageNet

  2. VCTK dataset(语音分类数据集)

  3. action sequence(deepMind的视频序列数据集)

• IJCAI19 - CLVSA A Convolutional LSTM Based Variational Sequence-to-Sequence Model with Attention for Predicting Trends of Financial Markets

  提出CLVSA，还没读

Anomaly detection

The work that is the most related to ours is AnoGAN (Schlegl et al., 2017).

…

Bergmann et al. (2019) compare standard AE reconstructions techniques
to AnoGAN, and observes that AnoGAN’s performances on anomaly localizations tasks are
poorer than AE’s due to the mode collapse tendency of GAN architectures. Interestingly, updates on
AnoGAN such as fast AnoGAN (Schlegl et al., 2019) or AnoVAEGAN (Baur et al., 2018) replaced
the gradient descent search of the optimal z with a learned encoder model, yielding an approach
very similar to the standard VAE reconstruction-based approaches, but with a reconstruction loss
learned by a discriminator, which is still prone to mode collapse (Thanh-Tung et al., 2019).

AnoGAN (Schlegl et al., 2017) Thomas Schlegl, Philipp Seeböck, Sebastian M Waldstein, Ursula Schmidt-Erfurth, and Georg
Langs. Unsupervised anomaly detection with generative adversarial networks to guide marker
discovery. In International Conference on Information Processing in Medical Imaging, pp. 146–157. Springer, 2017.

Bergmann et al. (2019) Paul Bergmann, Michael Fauser, David Sattlegger, and Carsten Steger. Mvtec ad—a comprehensive real-world
dataset for unsupervised anomaly detection. CVPR, 2019.

fast AnoGAN (Schlegl et al., 2019) Thomas Schlegl, Philipp Seeböck, Sebastian M. Waldstein, Georg Langs, and Ursula Schmidt-
Erfurth. f-anogan: Fast unsupervised anomaly detection with generative adversarial networks.
Medical Image Analysis, 54:30 – 44, 2019. ISSN 1361-8415. doi: https://doi.org/10.1016/j.
media.2019.01.010.

AnoVAEGAN (Baur et al., 2018) Christoph Baur, BenediktWiestler, Shadi Albarqouni, and Nassir Navab. Deep autoencoding models
for unsupervised anomaly segmentation in brain MR images. CoRR, abs/1804.04488, 2018.