add slides for kai's anc talk

anc.md

@@ -0,0 +1,227 @@
+---
+marp: true
+paginate: true
+---
+
+<style>
+section {
+  font-size: 24px;
+}
+.footnote {
+  font-size: 20px;
+}
+</style>
+
+# **G**enerative **Ra**tio **M**atching Networks
+
+Akash Srivastava$^{\ast,1,2}$, Kai Xu$^{\ast,3}$, Michael U. Gutmann$^{3}$, Charles Sutton$^{3,4,5}$
+
+<br><br><br><br><br><br>
+
+$\ast$ denotes equal contributions
+$^1$MIT-IBM Watson AI Lab $^2$IBM Research $^3$University of Edinburgh $^4$Google AI $^5$Alan Turing Institute
+
+<br>
+
+To appear in ICLR 2020; OpenReview: https://openreview.net/forum?id=SJg7spEYDS
+
+---
+
+## Introduction and motivations
+
+Implicit deep generative models: $x = \mathrm{NN}(z; \theta)$ where $z \sim$ noise
+
+Maximum mean discrepancy networks (MMD-nets)
+
+- :x: can only work well with **low-dimensional** data
+- :white_check_mark: are very **stable** to train by avoiding the saddle-point optimization problem
+
+Adversarial generative models (e.g. GANs, MMD-GANs)
+
+- :white_check_mark: can generate **high-dimensional** data such as natural images
+- :x: are very **difficult** to train due to the saddle-point optimization problem
+
+Q: Can we have two :white_check_mark::white_check_mark:? 
+A: Yes. Generative ratio matching (GRAM) is a *stable* learning algorithm for *implicit* deep generative models that does **not** involve a saddle-point optimization problem and therefore is easy to train :tada:.
+
+---
+
+## Background: **maximum mean discrepancy**
+
+The maximum mean discrepancy (MMD) between two distributions $p$ and $q$ is defined as
+$$
+\mathrm{MMD}_\mathcal{F}(p,q) = \sup_{f\in\mathcal{F}} \left(\mathbb{E}_p \lbrack f(x) \rbrack - \mathbb{E}_q \lbrack f(x) \rbrack \right)
+$$
+Gretton et al. (2012) shows that it is sufficient to choose $\mathcal{F}$ to be a unit ball in an reproducing kernel Hilbert space (RKHS) $\mathcal{R}$ with a characteristic kernel $k$ s.t.
+$$
+\mathrm{MMD}_\mathcal{F}(p, q) = 0 \iff p = q
+$$
+The empirical estimate of the (squared) MMD with $x_i \sim p$ and $y_j \sim q$ by Monte Carlo is
+$$
+\hat{\textmd{MMD}}^2_\mathcal{R}(p,q) =
+\frac{1}{N^2}\sum_{i=1}^N\sum_{i'=1}^N k(x_i,x_{i'})
+- \frac{2}{NM}\sum_{i=1}^N\sum_{j=1}^M  k(x_i, y_j)
+ + \frac{1}{M^2}\sum_{j=1}^M\sum_{j'=1}^M k(y_j,y_{j'})
+$$
+MMD-nets trains neural generators by minimizing this empirical estimate.
+
+---
+
+## Background: **density ratio estimation via moment matching**
+
+Density ratio estimation: find $\hat{r}(x) \approx r(x) = \frac{p(x)}{q(x)}$ with samples from $p$ and $q$
+
+Finite moments under the fixed design setup gives $\hat{\mathbf{r}}_q = [\hat{r}(x_1^q), ..., \hat{r}(x_M^q)]$ for $x^q \sim q$
+
+$$
+\min_r \left ( \int \phi(x)p(x) dx - \int \phi(x)r(x)q(x) dx \right )^2
+$$
+
+Huang et al. (2007) shows that by changing $\phi(x)$ to $k(x; .)$, where $k$ is a characteristic kernel in RKHS, we can match infinite moments and the optimization below agrees with the true $r(x)$
+
+$$
+\min_{r\in\mathcal{R}} \bigg \Vert \int k(x; .)p(x) dx - \int k(x; .)r(x)q(x) dx \bigg \Vert_{\mathcal{R}}^2
+$$
+
+Analytical solution: $\hat{\mathbf{r}} = \mathbf{K}_{q,q}^{-1}\mathbf{K}_{q,p} \mathbf{1}$, where $[\mathbf{K}_{p,q}]_{i,j} = k(x_i^p, x_j^q)$ given samples $\{x_i^p\}$ and $\{x_j^q\}$.
+
+---
+
+## GRAM: an overview
+
+Two targets in the training loop
+
+1. Learning a projection function $f_\theta$ that maps the data space into a low-dimensional manifold which preserves the density ratio between data and model.
+   - "Preserves": $\frac{p_x(x)}{q_x(x)} = \frac{\bar{p}(f_\theta(x))}{\bar{q}(f_\theta(x))}$, measured by $D(\theta) = \int q_x(x) \left( \frac{p_x(x)}{q_x(x)} - \frac{\bar{p}(f_\theta(x))}{\bar{q}(f_\theta(x))} \right)^2 dx$
+   - :question: $\frac{p_x(x)}{q_x(x)}$ is hard to estimate in the high-dimensional space ...
+2. Matching the model $G_\gamma$ to data in the low-dimensional manifold by minimizing MMD
+   - :thumbsup: MMD works well in low dimensional space
+   - $\mathrm{MMD} = 0$ :arrow_right: $\frac{\bar{p}(f_\theta(x))}{\bar{q}(f_\theta(x))} = 1$ :arrow_right: $\frac{p_x(x)}{q_x(x)} = 1$
+
+Both with empirical estimates based on samples from the data $\{x_i^p\}$ and the model $\{x_j^q\}$.
+
+$f_\theta$ and $G_\gamma$ are simultaneously updated.
+
+---
+
+## GRAM: tractable ratio matching
+
+:one: Learning the projection function $f_\theta(x)$ by minimizing the squared difference
+$$
+\begin{aligned}
+D(\theta) 
+&= \int q_x(x) \left( \frac{p_x(x)}{q_x(x)} - \frac{\bar{p}(f_\theta(x))}{\bar{q}(f_\theta(x))} \right)^2 dx \\
+&= C - 2 \int p_x(x) \frac{\bar{p}(f_\theta(x))}{\bar{q}(f_\theta(x))} dx + \int q_x(x) \left( \frac{\bar{p}(f_\theta(x))}{\bar{q}(f_\theta(x))} \right)^2 dx \\
+&= C - 2 \int \bar{p}(f_\theta(x)) \frac{\bar{p}(f_\theta(x))}{\bar{q}(f_\theta(x))} df_\theta(x) + \int \bar{q}(f_\theta(x)) \left( \frac{\bar{p}(f_\theta(x))}{\bar{q}(f_\theta(x))} \right)^2 df_\theta(x) \\
+&= C' -  \left( \int \bar{q}(f_\theta(x)) \left( \frac{\bar{p}(f_\theta(x))}{\bar{q}(f_\theta(x))} \right)^2 df_\theta(x) - 1 \right) = C' - \mathrm{PD}(\bar{q}, \bar{p})
+\end{aligned}
+$$
+... or by equivalently maximizing the Pearson divergence :smile:.
+
+A reminder on LOTUS: $\int p(x) g(f(x)) dx = \int p(f(x)) g(f(x)) d f(x)$
+
+<br>
+
+<div class="footnote">
+[1]: A derivation of the reverse order for a special case of projection functions was also shown in (Sugiyama et al., 2011).
+</dvi>
+
+---
+
+## GRAM: Pearson divergence maximization
+
+Monte Carlo approximation of PD
+$$
+\mathrm{PD}(\bar{q}, \bar{p}) = \int \bar{q}(f_\theta(x)) \left( \frac{\bar{p}(f_\theta(x))}{\bar{q}(f_\theta(x))} \right)^2 df_\theta(x) - 1  \approx \frac{1}{N} \sum_{i=1}^N \left( \frac{\bar{p}(f_\theta(x_i^q))}{\bar{q}(f_\theta(x_i^q))} \right)^2 - 1
+$$
+where $x^q_i \sim q_x$ or equivalently $f_\theta(x^q_i) \sim \bar{q}$.
+
+Given samples $\{x_i^p\}$ and $\{x_j^q\}$, we use the density ratio estimator based on infinite moments matching (Huang et al., 2007, Sugiyama et al., 2012) under the fixed-design setup
+$$
+\hat{\mathbf{r}}_{q,_\theta} = \mathbf{K}^{-1}_{q,q} \mathbf{K}_{q,p}\mathbf{1} = [\hat{r}_\theta(x_1^q), ..., \hat{r}_\theta(x_M^q)]^\top
+$$
+where $[\mathbf{K}_{p,q}]_{i,j} = k(f_\theta(x_i^p), f_\theta(x_j^q))$ and  $r_\theta(x) = \frac{\bar{p}(f_\theta(x))}{\bar{q}(f_\theta(x))}$.
+
+---
+
+## GRAM: matching projected model to projected data
+
+:two: Minimizing the empirical estimator of MMD in the low-dimensional manifold
+
+$$
+\begin{aligned}
+\min_\gamma \Bigg[&\frac{1}{N^2}\sum_{i=1}^N\sum_{i'=1}^N k(f_\theta(x_i),f_\theta(x_{i'})) 
+- \frac{2}{NM}\sum_{i=1}^N\sum_{j=1}^M  k(f_\theta(x_i), f_\theta(G_\gamma(z_j)))\\
+&\quad + \frac{1}{M^2}\sum_{j=1}^M\sum_{j'=1}^M k(f_\theta(G_\gamma(z_j)),f_\theta(G_\gamma(z_{j'}))) \Bigg ]
+\end{aligned}
+$$
+
+with respect to its parameters $\gamma$.
+
+---
+
+## GRAM: the complete algorithm
+
+Loop until convergence
+
+1. Sample a minibatch of data and generate samples from $G_\gamma$
+2. Project data and generated samples using $f_\theta$
+3. Compute the kernel Gram matrices using Gaussian kernels in the projected space
+4. Compute the objectives for $f_\theta$ and $G_\gamma$ using the same kernel Gram matrices
+5. Backprop two objectives to get the gradients for $\theta$ and $\gamma$
+6. Perform gradient update for $\theta$ and $\gamma$
+
+<br>
+
+:sunglasses: Fun fact: the objectives in our GRAM algorithm both heavily relies on the use of kernel Gram matrices.
+
+---
+
+## How do GRAM-nets compare to other deep generative models
+
+| GAN | MMD-net | MMD-GAN | GRAM-net |
+| - | - | - | - |
+| ![Computation graph (GAN)](comp_graph-gan.png) | ![Computation graph (MMD-net)](comp_graph-mmdnet.png) | ![Computation graph (MMD-GAN)](comp_graph-mmdgan.png) | ![Computation graph (GRAM-net)](comp_graph-gramnet.png) |
+
+---
+
+## Illustration of GRAM training
+
+| | |
+| - | - |
+| ![Ring: training process](ring_process.png) | ![width:350px](trace.png) |
+
+Blue: data, Orange: samples
+Top: original, Bottom: projected
+
+---
+
+## Evaluations: the stability of models
+
+![Ring: stability on GAN and GRAM-nets](stability_gan_gramnet.png)
+
+x-axis = noise dimension and y-axis = generator layer size
+
+---
+
+## Evaluations: the stability of models (continued)
+
+![Ring: stability on MMD-nets and MMD-GANs](stability_mmdnet_mmdgan.png)
+
+x-axis = noise dimension and y-axis = generator layer size
+
+---
+
+## Quantitative results: sample quality
+
+![FID table](table_image.png)
+
+---
+
+## Qualitative results: random samples
+
+![Samples (CIFAR10)](cifar10.png) ![Samples (CelebA)](celeba.png)
+
+---
+
+## The end