We introduce the Generalized Energy Based Model (GEBM) for generative
modelling. These models combine two trained components: a base distribution
(generally an implicit model), which can learn the support of data with low
intrinsic dimension in a high dimensional space; and an energy function, to
refine the probability mass on the learned support. Both the energy function
and base jointly constitute the final model, unlike GANs, which retain only the
base distribution (the "generator"). GEBMs are trained by alternating between
learning the energy and the base. We show that both training stages are
well-defined: the energy is learned by maximising a generalized likelihood, and
the resulting energy-based loss provides informative gradients for learning the
base. Samples from the posterior on the latent space of the trained model can
be obtained via MCMC, thus finding regions in this space that produce better
quality samples. Empirically, the GEBM samples on image-generation tasks are of
much better quality than those from the learned generator alone, indicating
that all else being equal, the GEBM will outperform a GAN of the same
complexity. When using normalizing flows as base measures, GEBMs succeed on
density modelling tasks, returning comparable performance to direct maximum
likelihood of the same networks.

本文介绍了广义能量模型（GEBM）的产生式建模方法，指出了其在图像生成任务和密度建模任务中的应用和比 GANS 更好的性能表现。

广义能量基模型

Generalized Energy Based Models

We propose to reinterpret a standard discriminative classifier of p(y|x) as
an energy based model for the joint distribution p(x,y). In this setting, the
standard class probabilities can be easily computed as well as unnormalized
values of p(x) and p(x|y). Within this framework, standard discriminative
architectures may beused and the model can also be trained on unlabeled data.
We demonstrate that energy based training of the joint distribution improves
calibration, robustness, andout-of-distribution detection while also enabling
our models to generate samplesrivaling the quality of recent GAN approaches. We
improve upon recently proposed techniques for scaling up the training of energy
based models and presentan approach which adds little overhead compared to
standard classification training. Our approach is the first to achieve
performance rivaling the state-of-the-artin both generative and discriminative
learning within one hybrid model.

本文提出将标准的判别分类器重新解释为联合分布的能量模型，以提高校准性、鲁棒性和生成学习的绩效。

你的分类器实际上是基于能量的模型，你应该使用基于能量的模型的方法对待它

Your Classifier is Secretly an Energy Based Model and You Should Treat  it Like One

In this paper, we propose a structured image inpainting method employing an
energy based model. In order to learn structural relationship between patterns
observed in images and missing regions of the images, we employ an energy-based
structured prediction method. The structural relationship is learned by
minimizing an energy function which is defined by a simple convolutional neural
network. The experimental results on various benchmark datasets show that our
proposed method significantly outperforms the state-of-the-art methods which
use Generative Adversarial Networks (GANs). We obtained 497.35 mean squared
error (MSE) on the Olivetti face dataset compared to 833.0 MSE provided by the
state-of-the-art method. Moreover, we obtained 28.4 dB peak signal to noise
ratio (PSNR) on the SVHN dataset and 23.53 dB on the CelebA dataset, compared
to 22.3 dB and 21.3 dB, provided by the state-of-the-art methods, respectively.
The code is publicly available.

本文提出了一种采用能量模型的结构化图像修复方法，通过卷积神经网络学习图像的结构关系以及缺失区域，并在多个基准数据集上进行实验，发现其明显优于使用生成对抗网络的现有方法。

深度结构化基于能量的图像修复

Deep Structured Energy-Based Image Inpainting

In this paper, we study deep generative models for effective unsupervised
learning. We propose VGAN, which works by minimizing a variational lower bound
of the negative log likelihood (NLL) of an energy based model (EBM), where the
model density $p(\mathbf{x})$ is approximated by a variational distribution
$q(\mathbf{x})$ that is easy to sample from. The training of VGAN takes a two
step procedure: given $p(\mathbf{x})$, $q(\mathbf{x})$ is updated to maximize
the lower bound; $p(\mathbf{x})$ is then updated one step with samples drawn
from $q(\mathbf{x})$ to decrease the lower bound. VGAN is inspired by the
generative adversarial networks (GANs), where $p(\mathbf{x})$ corresponds to
the discriminator and $q(\mathbf{x})$ corresponds to the generator, but with
several notable differences. We hence name our model variational GANs (VGANs).
VGAN provides a practical solution to training deep EBMs in high dimensional
space, by eliminating the need of MCMC sampling. From this view, we are also
able to identify causes to the difficulty of training GANs and propose viable
solutions. \footnote{Experimental code is available at
this https URL}

该论文研究了深度生成模型在有效的无监督学习中的应用，提出了 VGAN 模型，通过最小化能量密度函数的负对数似然的变分下界，使得模型能够用 Variational Distribution 进行采样，从而可以更方便地训练模型。