Images generated by diffusion models like Stable Diffusion are increasingly
widespread. Recent works and even lawsuits have shown that these models are
prone to replicating their training data, unbeknownst to the user. In this
paper, we first analyze this memorization problem in text-to-image diffusion
models. While it is widely believed that duplicated images in the training set
are responsible for content replication at inference time, we observe that the
text conditioning of the model plays a similarly important role. In fact, we
see in our experiments that data replication often does not happen for
unconditional models, while it is common in the text-conditional case.
Motivated by our findings, we then propose several techniques for reducing data
replication at both training and inference time by randomizing and augmenting
image captions in the training set.

分析了文本到图像扩散模型中的记忆问题，提出了减少训练和推理时间中数据复制的几种技术。

理解和减轻扩散模型中的复制现象

Understanding and Mitigating Copying in Diffusion Models

Recent theoretical work has established connections between over-parametrized
neural networks and linearized models governed by he Neural Tangent Kernels
(NTKs). NTK theory leads to concrete convergence and generalization results,
yet the empirical performance of neural networks are observed to exceed their
linearized models, suggesting insufficiency of this theory.
Towards closing this gap, we investigate the training of over-parametrized
neural networks that are beyond the NTK regime yet still governed by the Taylor
expansion of the network. We bring forward the idea of \emph{randomizing} the
neural networks, which allows them to escape their NTK and couple with
quadratic models. We show that the optimization landscape of randomized
two-layer networks are nice and amenable to escaping-saddle algorithms. We
prove concrete generalization and expressivity results on these randomized
networks, which lead to sample complexity bounds (of learning certain simple
functions) that match the NTK and can in addition be better by a dimension
factor when mild distributional assumptions are present. We demonstrate that
our randomization technique can be generalized systematically beyond the
quadratic case, by using it to find networks that are coupled with higher-order
terms in their Taylor series.

该研究探讨了超参数化神经网络的训练，通过随机化神经网络，使其模型不再受限于 NTK，允许其与二次模型进行耦合，并且证明了产生的随机网络的优化具有良好的局面性态，且样本复杂度与 NTK 相匹配，同时在分布特定条件下可更为优秀。