Exemplar-Free Class Incremental Learning is a highly challenging setting
where replay memory is unavailable. Methods relying on frozen feature
extractors have drawn attention recently in this setting due to their
impressive performances and lower computational costs. However, those methods
are highly dependent on the data used to train the feature extractor and may
struggle when an insufficient amount of classes are available during the first
incremental step. To overcome this limitation, we propose to use a pre-trained
text-to-image diffusion model in order to generate synthetic images of future
classes and use them to train the feature extractor. Experiments on the
standard benchmarks CIFAR100 and ImageNet-Subset demonstrate that our proposed
method can be used to improve state-of-the-art methods for exemplar-free class
incremental learning, especially in the most difficult settings where the first
incremental step only contains few classes. Moreover, we show that using
synthetic samples of future classes achieves higher performance than using real
data from different classes, paving the way for better and less costly
pre-training methods for incremental learning.

通过使用预训练的文本到图像扩散模型生成未来类别的合成图像并使用它们训练特征提取器，我们提出的方法改进了无典范类增量学习的最新方法，特别是在只包含少量类别的最困难的情况下。此外，我们还表明使用未来类别的合成样本比使用不同类别的真实数据能够实现更高的性能，为增量学习的更好和更低成本的预训练方法铺平了道路。

面向未来的班级增量学习

Future-Proofing Class Incremental Learning

Text-conditioned image editing has recently attracted considerable interest.
However, most methods are currently either limited to specific editing types
(e.g., object overlay, style transfer), or apply to synthetically generated
images, or require multiple input images of a common object. In this paper we
demonstrate, for the very first time, the ability to apply complex (e.g.,
non-rigid) text-guided semantic edits to a single real image. For example, we
can change the posture and composition of one or multiple objects inside an
image, while preserving its original characteristics. Our method can make a
standing dog sit down or jump, cause a bird to spread its wings, etc. -- each
within its single high-resolution natural image provided by the user. Contrary
to previous work, our proposed method requires only a single input image and a
target text (the desired edit). It operates on real images, and does not
require any additional inputs (such as image masks or additional views of the
object). Our method, which we call "Imagic", leverages a pre-trained
text-to-image diffusion model for this task. It produces a text embedding that
aligns with both the input image and the target text, while fine-tuning the
diffusion model to capture the image-specific appearance. We demonstrate the
quality and versatility of our method on numerous inputs from various domains,
showcasing a plethora of high quality complex semantic image edits, all within
a single unified framework.

本文提出了一种名为 “Imagic” 的方法，该方法仅需要单个输入图像和目标文本，就能针对单张高分辨率的真实图像进行复杂的文本引导语义编辑，其采用预训练的文本到图像扩散模型进行柔性编辑，不需任何额外的输入或附加视图，从而展示了各个领域的高质量复杂语义图像编辑.