Face aging is an ill-posed problem because multiple plausible aging patterns
may correspond to a given input. Most existing methods often produce one
deterministic estimation. This paper proposes a novel CLIP-driven Pluralistic
Aging Diffusion Autoencoder (PADA) to enhance the diversity of aging patterns.
First, we employ diffusion models to generate diverse low-level aging details
via a sequential denoising reverse process. Second, we present Probabilistic
Aging Embedding (PAE) to capture diverse high-level aging patterns, which
represents age information as probabilistic distributions in the common CLIP
latent space. A text-guided KL-divergence loss is designed to guide this
learning. Our method can achieve pluralistic face aging conditioned on
open-world aging texts and arbitrary unseen face images. Qualitative and
quantitative experiments demonstrate that our method can generate more diverse
and high-quality plausible aging results.

本文提出了一种 CLIP-driven Pluralistic Aging Diffusion Autoencoder（PADA）方法，以增强面部衰老模式的多样性，其中使用扩散模型生成不同的低级衰老细节，并提出概率衰老嵌入（PAE）以捕获不同的高级衰老模式，在通用的 CLIP 潜在空间中使用文本引导 KL 散度损失进行学习，该方法可以根据任意未见面部图像和开放世界衰老文本实现多元化的面部衰老。

多元化老化扩散自编码器

Pluralistic Aging Diffusion Autoencoder

Natural Language Processing algorithms have made incredible progress, but
they still struggle when applied to out-of-distribution examples. We address a
challenging and underexplored version of this domain adaptation problem, where
an algorithm is trained on several source domains, and then applied to examples
from unseen domains that are unknown at training time. Particularly, no
examples, labeled or unlabeled, or any other knowledge about the target domain
are available to the algorithm at training time. We present PADA: An
example-based autoregressive Prompt learning algorithm for on-the-fly
Any-Domain Adaptation, based on the T5 language model. Given a test example,
PADA first generates a unique prompt for it and then, conditioned on this
prompt, labels the example with respect to the NLP prediction task. PADA is
trained to generate a prompt which is a token sequence of unrestricted length,
consisting of Domain Related Features (DRFs) that characterize each of the
source domains. Intuitively, the generated prompt is a unique signature that
maps the test example to a semantic space spanned by the source domains. In
experiments with 3 tasks (text classification and sequence tagging), for a
total of 14 multi-source adaptation scenarios, PADA substantially outperforms
strong baselines.

我们提出了一个基于自动回归 Prompt 学习算法的 PADA，用于实时任何领域自适应，其中生成的唯一提示作为源域特征的连续令牌序列，PADA 在多个任务中显着优于强基准。