Learning image classification and image generation using the same set of
network parameters is a challenging problem. Recent advanced approaches perform
well in one task often exhibit poor performance in the other. This work
introduces an energy-based classifier and generator, namely EGC, which can
achieve superior performance in both tasks using a single neural network.
Unlike a conventional classifier that outputs a label given an image (i.e., a
conditional distribution $p(y|\mathbf{x})$), the forward pass in EGC is a
classifier that outputs a joint distribution $p(\mathbf{x},y)$, enabling an
image generator in its backward pass by marginalizing out the label $y$. This
is done by estimating the energy and classification probability given a noisy
image in the forward pass, while denoising it using the score function
estimated in the backward pass. EGC achieves competitive generation results
compared with state-of-the-art approaches on ImageNet-1k, CelebA-HQ and LSUN
Church, while achieving superior classification accuracy and robustness against
adversarial attacks on CIFAR-10. This work represents the first successful
attempt to simultaneously excel in both tasks using a single set of network
parameters. We believe that EGC bridges the gap between discriminative and
generative learning.

本文提出了一种名为 EGC 的基于能量的分类器和生成器，可通过单个神经网络同时实现优越的图像分类和图像生成效果。

通过单一基于能量的模型完成图像生成和分类

EGC: Image Generation and Classification via a Single Energy-Based Model

We introduce the "Energy-based Generative Adversarial Network" model (EBGAN)
which views the discriminator as an energy function that attributes low
energies to the regions near the data manifold and higher energies to other
regions. Similar to the probabilistic GANs, a generator is seen as being
trained to produce contrastive samples with minimal energies, while the
discriminator is trained to assign high energies to these generated samples.
Viewing the discriminator as an energy function allows to use a wide variety of
architectures and loss functionals in addition to the usual binary classifier
with logistic output. Among them, we show one instantiation of EBGAN framework
as using an auto-encoder architecture, with the energy being the reconstruction
error, in place of the discriminator. We show that this form of EBGAN exhibits
more stable behavior than regular GANs during training. We also show that a
single-scale architecture can be trained to generate high-resolution images.

介绍了一种名为 “能量基生成对抗网络”（EBGAN）的模型，将判别器视为能量函数，用于选定数据流形附近区域的低能量和其他区域的高能量，可以使用多种体系结构和损失函数，本文采用自编码器架构，以重构误差作为能量，实现了高分辨率图像的生成。