Deep generative models have made tremendous progress in modeling complex
data, often exhibiting generation quality that surpasses a typical human's
ability to discern the authenticity of samples. Undeniably, a key driver of
this success is enabled by the massive amounts of web-scale data consumed by
these models. Due to these models' striking performance and ease of
availability, the web will inevitably be increasingly populated with synthetic
content. Such a fact directly implies that future iterations of generative
models must contend with the reality that their training is curated from both
clean data and artificially generated data from past models. In this paper, we
develop a framework to rigorously study the impact of training generative
models on mixed datasets (of real and synthetic data) on their stability. We
first prove the stability of iterative training under the condition that the
initial generative models approximate the data distribution well enough and the
proportion of clean training data (w.r.t. synthetic data) is large enough. We
empirically validate our theory on both synthetic and natural images by
iteratively training normalizing flows and state-of-the-art diffusion models on
CIFAR10 and FFHQ.

本文开发了一个框架，以严格研究在混合数据集（包括真实和合成数据）上训练生成模型对它们的稳定性的影响。通过在 CIFAR10 和 FFHQ 上迭代训练常规流和最先进的扩散模型，我们在合成和自然图像上经验验证了我们的理论。

关于迭代重新训练生成模型的稳定性

On the Stability of Iterative Retraining of Generative Models on their  own Data

Entity Resolution (ER) is the problem of determining when two entities refer
to the same underlying entity. The problem has been studied for over 50 years,
and most recently, has taken on new importance in an era of large,
heterogeneous 'knowledge graphs' published on the Web and used widely in
domains as wide ranging as social media, e-commerce and search. This chapter
will discuss the specific problem of named ER in the context of personal
knowledge graphs (PKGs). We begin with a formal definition of the problem, and
the components necessary for doing high-quality and efficient ER. We also
discuss some challenges that are expected to arise for Web-scale data. Next, we
provide a brief literature review, with a special focus on how existing
techniques can potentially apply to PKGs. We conclude the chapter by covering
some applications, as well as promising directions for future research.

实体消解是确定两个实体是否指向相同基础实体的问题。本章将讨论在个人知识图谱（PKG）环境下的命名实体消解问题，包括问题的形式化定义、进行高质量和高效实体消解所需的组件、面临的挑战、现有技术在 PKG 中的应用、一些应用案例以及未来研究的方向。

个人知识图谱中的命名实体解析

Named Entity Resolution in Personal Knowledge Graphs

Image-text contrastive learning models such as CLIP have demonstrated strong
task transfer ability. The high generality and usability of these visual models
is achieved via a web-scale data collection process to ensure broad concept
coverage, followed by expensive pre-training to feed all the knowledge into
model weights. Alternatively, we propose REACT, REtrieval-Augmented
CusTomization, a framework to acquire the relevant web knowledge to build
customized visual models for target domains. We retrieve the most relevant
image-text pairs (~3% of CLIP pre-training data) from the web-scale database as
external knowledge, and propose to customize the model by only training new
modualized blocks while freezing all the original weights. The effectiveness of
REACT is demonstrated via extensive experiments on classification, retrieval,
detection and segmentation tasks, including zero, few, and full-shot settings.
Particularly, on the zero-shot classification task, compared with CLIP, it
achieves up to 5.4% improvement on ImageNet and 3.7% on the ELEVATER benchmark
(20 datasets).

提出一种名为 REACT 的框架用于获取相关网络知识以构建目标领域的定制视觉模型。该框架通过检索与预训练数据集最相关的图像 - 文本对作为外部知识源，并仅训练新的模块化块，冻结所有原始权重，证明了其在零、少、全样本设置下显著提高了分类、检索、检测和分割任务的成功率。