Shape and texture are two prominent and complementary cues for recognizing
objects. Nonetheless, Convolutional Neural Networks are often biased towards
either texture or shape, depending on the training dataset. Our ablation shows
that such bias degenerates model performance. Motivated by this observation, we
develop a simple algorithm for shape-texture debiased learning. To prevent
models from exclusively attending on a single cue in representation learning,
we augment training data with images with conflicting shape and texture
information (eg, an image of chimpanzee shape but with lemon texture) and, most
importantly, provide the corresponding supervisions from shape and texture
simultaneously.
Experiments show that our method successfully improves model performance on
several image recognition benchmarks and adversarial robustness. For example,
by training on ImageNet, it helps ResNet-152 achieve substantial improvements
on ImageNet (+1.2%), ImageNet-A (+5.2%), ImageNet-C (+8.3%) and
Stylized-ImageNet (+11.1%), and on defending against FGSM adversarial attacker
on ImageNet (+14.4%). Our method also claims to be compatible with other
advanced data augmentation strategies, eg, Mixup, and CutMix. The code is
available here: this https URL

本研究通过引入与图片纹理信息矛盾的图片来训练卷积神经网络，解决了网络对于形状或纹理的偏向性问题，并通过在多个图像识别基准测试和对抗性鲁棒性方面的实验表明，成功地提高了模型的表现。