Previous work in Neural Loss Function Search (NLFS) has shown a lack of
correlation between smaller surrogate functions and large convolutional neural
networks with massive regularization. We expand upon this research by revealing
another disparity that exists, correlation between different types of image
augmentation techniques. We show that different loss functions can perform well
on certain image augmentation techniques, while performing poorly on others. We
exploit this disparity by performing an evolutionary search on five types of
image augmentation techniques in the hopes of finding image augmentation
specific loss functions. The best loss functions from each evolution were then
taken and transferred to WideResNet-28-10 on CIFAR-10 and CIFAR-100 across each
of the five image augmentation techniques. The best from that were then taken
and evaluated by fine-tuning EfficientNetV2Small on the CARS, Oxford-Flowers,
and Caltech datasets across each of the five image augmentation techniques.
Multiple loss functions were found that outperformed cross-entropy across
multiple experiments. In the end, we found a single loss function, which we
called the inverse bessel logarithm loss, that was able to outperform
cross-entropy across the majority of experiments.

通过进行演化搜索找到了一种名为逆贝塞尔对数损失的单一损失函数，它在多数实验中表现优于交叉熵。

针对特定图像增强技术演化的损失函数

Evolving Loss Functions for Specific Image Augmentation Techniques

Data augmentations are useful in closing the sim-to-real domain gap when
training on synthetic data. This is because they widen the training data
distribution, thus encouraging the model to generalize better to other domains.
Many image augmentation techniques exist, parametrized by different settings,
such as strength and probability. This leads to a large space of different
possible augmentation policies. Some policies work better than others for
overcoming the sim-to-real gap for specific datasets, and it is unclear why.
This paper presents two different interpretable metrics that can be combined to
predict how well a certain augmentation policy will work for a specific
sim-to-real setting, focusing on object detection. We validate our metrics by
training many models with different augmentation policies and showing a strong
correlation with performance on real data. Additionally, we introduce
GeneticAugment, a genetic programming method that can leverage these metrics to
automatically design an augmentation policy for a specific dataset without
needing to train a model.

使用可解释的度量方法，结合遗传编程技术，预测适用于特定 sim-to-real 场景的数据增强策略，并在无需训练模型的情况下为特定数据集设计数据增强策略。

基因学习用于设计模拟到真实数据增强

Genetic Learning for Designing Sim-to-Real Data Augmentations

Zero-Shot Learning (ZSL) aims to recognize unseen classes by generalizing the
knowledge, i.e., visual and semantic relationships, obtained from seen classes,
where image augmentation techniques are commonly applied to improve the
generalization ability of a model. However, this approach can also cause
adverse effects on ZSL since the conventional augmentation techniques that
solely depend on single-label supervision is not able to maintain semantic
information and result in the semantic distortion issue consequently. In other
words, image argumentation may falsify the semantic (e.g., attribute)
information of an image. To take the advantage of image augmentations while
mitigating the semantic distortion issue, we propose a novel ZSL approach by
Harnessing Adversarial Samples (HAS). HAS advances ZSL through adversarial
training which takes into account three crucial aspects: (1) robust generation
by enforcing augmentations to be similar to negative classes, while maintaining
correct labels, (2) reliable generation by introducing a latent space
constraint to avert significant deviations from the original data manifold, and
(3) diverse generation by incorporating attribute-based perturbation by
adjusting images according to each semantic attribute's localization. Through
comprehensive experiments on three prominent zero-shot benchmark datasets, we
demonstrate the effectiveness of our adversarial samples approach in both ZSL
and Generalized Zero-Shot Learning (GZSL) scenarios. Our source code is
available at this https URL

零样本学习（ZSL）通过从已见类别中获取的知识（如视觉和语义关系）进行泛化，旨在识别未见类别，但常用的图像增强技术可能对 ZSL 产生负面影响，我们提出一种利用对抗样本的新型 ZSL 方法来解决语义扭曲问题，在三个著名的零样本基准数据集上进行了综合实验，证明了对抗样本方法在 ZSL 和广义零样本学习（GZSL）场景中的有效性。