The integration of discrete algorithmic components in deep learning
architectures has numerous applications. Recently, Implicit Maximum Likelihood
Estimation (IMLE, Niepert, Minervini, and Franceschi 2021), a class of gradient
estimators for discrete exponential family distributions, was proposed by
combining implicit differentiation through perturbation with the path-wise
gradient estimator. However, due to the finite difference approximation of the
gradients, it is especially sensitive to the choice of the finite difference
step size, which needs to be specified by the user. In this work, we present
Adaptive IMLE (AIMLE), the first adaptive gradient estimator for complex
discrete distributions: it adaptively identifies the target distribution for
IMLE by trading off the density of gradient information with the degree of bias
in the gradient estimates. We empirically evaluate our estimator on synthetic
examples, as well as on Learning to Explain, Discrete Variational
Auto-Encoders, and Neural Relational Inference tasks. In our experiments, we
show that our adaptive gradient estimator can produce faithful estimates while
requiring orders of magnitude fewer samples than other gradient estimators.

本研究提出了第一个自适应梯度估计器 AIMLE，用于复杂离散分布的 IMLE，通过交换梯度信息密度和估计偏差的程度，自适应地确定目标分布，实验证明 AIMLE 能产生忠实的梯度估计，而需要比其他梯度估计器少数个数量级的样本。

离散潜变量模型自适应扰动梯度估计

Adaptive Perturbation-Based Gradient Estimation for Discrete Latent Variable Models

Combining discrete probability distributions and combinatorial optimization
problems with neural network components has numerous applications but poses
several challenges. We propose Implicit Maximum Likelihood Estimation (I-MLE),
a framework for end-to-end learning of models combining discrete exponential
family distributions and differentiable neural components. I-MLE is widely
applicable as it only requires the ability to compute the most probable states
and does not rely on smooth relaxations. The framework encompasses several
approaches such as perturbation-based implicit differentiation and recent
methods to differentiate through black-box combinatorial solvers. We introduce
a novel class of noise distributions for approximating marginals via
perturb-and-MAP. Moreover, we show that I-MLE simplifies to maximum likelihood
estimation when used in some recently studied learning settings that involve
combinatorial solvers. Experiments on several datasets suggest that I-MLE is
competitive with and often outperforms existing approaches which rely on
problem-specific relaxations.

该论文提出了一种基于离散指数族分布和可微神经组件相结合的模型的端到端学习框架 Implicit Maximum Likelihood Estimation（I-MLE），并介绍了一种新型的噪声分布 perturb-and-MAP，该框架在几个数据集上表现良好，并且与依赖于特定问题松弛的现有方法相比竞争力强。

隐式最大似然估计：通过离散指数族分布反向传播

Implicit MLE: Backpropagating Through Discrete Exponential Family  Distributions

Many tasks in computer vision and graphics fall within the framework of
conditional image synthesis. In recent years, generative adversarial nets
(GANs) have delivered impressive advances in quality of synthesized images.
However, it remains a challenge to generate both diverse and plausible images
for the same input, due to the problem of mode collapse. In this paper, we
develop a new generic multimodal conditional image synthesis method based on
Implicit Maximum Likelihood Estimation (IMLE) and demonstrate improved
multimodal image synthesis performance on two tasks, single image
super-resolution and image synthesis from scene layouts. We make our
implementation publicly available.

本文提出了基于 IMLE 的新型多模式条件图像合成方法，并在两个任务中演示了更好的多模式图像合成性能，即单幅图像超分辨率和从场景布局中的图像合成。

基于条件隐式极大似然估计的多模态图像合成

Multimodal Image Synthesis with Conditional Implicit Maximum Likelihood  Estimation

Most existing methods for conditional image synthesis are only able to
generate a single plausible image for any given input, or at best a fixed
number of plausible images. In this paper, we focus on the problem of
generating images from semantic segmentation maps and present a simple new
method that can generate an arbitrary number of images with diverse appearance
for the same semantic layout. Unlike most existing approaches which adopt the
GAN framework, our method is based on the recently introduced Implicit Maximum
Likelihood Estimation (IMLE) framework. Compared to the leading approach, our
method is able to generate more diverse images while producing fewer artifacts
despite using the same architecture. The learned latent space also has sensible
structure despite the lack of supervision that encourages such behaviour.
Videos and code are available at
this https URL

本文研究了条件图像合成中的多样性问题，提出了基于 IMLE 框架的新方法，用于从语义分割映射生成具有不同外观的图像，并能够生成更多样化的图像，同时产生更少的伪影，具有合理的潜在空间结构。