Image ordinal regression has been mainly studied along the line of exploiting
the order of categories. However, the issues of class imbalance and category
overlap that are very common in ordinal regression were largely overlooked. As
a result, the performance on minority categories is often unsatisfactory. In
this paper, we propose a novel framework called CIG based on controllable image
generation to directly tackle these two issues. Our main idea is to generate
extra training samples with specific labels near category boundaries, and the
sample generation is biased toward the less-represented categories. To achieve
controllable image generation, we seek to separate structural and categorical
information of images based on structural similarity, categorical similarity,
and reconstruction constraints. We evaluate the effectiveness of our new CIG
approach in three different image ordinal regression scenarios. The results
demonstrate that CIG can be flexibly integrated with off-the-shelf image
encoders or ordinal regression models to achieve improvement, and further, the
improvement is more significant for minority categories.

本文提出了一种基于可控图像生成（CIG）的新型框架，它可以生成靠近类别边界的具有特定标签的额外训练样本，并且偏向于生成不常见的类别，从而更好地解决了图像序列回归中常见的类别不平衡和重叠问题。我们的实验结果表明，CIG 方法可以与预训练图像编码器或序列回归模型灵活集成，并且对于不常见的类别，改进效果更显著。

具有可控图像生成的鲁棒图像序列回归

Robust Image Ordinal Regression with Controllable Image Generation

Recent research has shown that it is possible to find interpretable
directions in the latent spaces of pre-trained Generative Adversarial Networks
(GANs). These directions enable controllable image generation and support a
wide range of semantic editing operations, such as zoom or rotation. The
discovery of such directions is often done in a supervised or semi-supervised
manner and requires manual annotations which limits their use in practice. In
comparison, unsupervised discovery allows finding subtle directions that are
difficult to detect a priori. In this work, we propose a contrastive
learning-based approach to discover semantic directions in the latent space of
pre-trained GANs in a self-supervised manner. Our approach finds semantically
meaningful dimensions comparable with state-of-the-art methods.

本研究提出了基于对比学习的方法，以自我监督的方式在预先训练的生成对抗网络的潜在空间中发现语义方向，并发现了与现有最先进方法可比较的有意义的语义维度。

LatentCLR：一种用于无监督发现可解释方向的对比学习方法

LatentCLR: A Contrastive Learning Approach for Unsupervised Discovery of  Interpretable Directions

We propose an interactive image-manipulation system with natural language
instruction, which can generate a target image from a source image and an
instruction that describes the difference between the source and the target
image. The system makes it possible to modify a generated image interactively
and make natural language conditioned image generation more controllable. We
construct a neural network that handles image vectors in latent space to
transform the source vector to the target vector by using the vector of
instruction. The experimental results indicate that the proposed framework
successfully generates the target image by using a source image and an
instruction on manipulation in our dataset.

本论文提出了能够通过自然语言指令生成目标图像的交互式图像处理系统，使用了神经网络处理潜在空间中的图像向量将源向量转换为目标向量，成功地在我们的数据集中利用源图像和操作指令生成目标图像，从而实现更可控、更实用的自然语言条件下的图像生成。