Abstract
Domain generalization in image classification is a crucial challenge, with models often failing to generalize well across unseen datasets. We address this issue by introducing a neuro-inspired Neural Response Normalization (NeuRN) layer which draws inspiration from neurons in the mammalian visual cortex, which aims to enhance the performance of deep learning architectures on unseen target domains by training deep learning models on a source domain. The performance of these models is considered as a baseline and then compared against models integrated with NeuRN on image classification tasks. We perform experiments across a range of deep learning architectures, including ones derived from Neural Architecture Search and Vision Transformer. Additionally, in order to shortlist models for our experiment from amongst the vast range of deep neural networks available which have shown promising results, we also propose a novel method that uses the Needleman-Wunsch algorithm to compute similarity between deep learning architectures. Our results demonstrate the effectiveness of NeuRN by showing improvement against baseline in cross-domain image classification tasks. Our framework attempts to establish a foundation for future neuro-inspired deep learning models.
Abstract (translated)
领域泛化在图像分类中是一个关键的挑战，模型往往难以很好地推广到未见过的数据集上。为了解决这个问题，我们引入了一种神经启发式的神经响应归一化（NeuRN）层，这种层借鉴了哺乳动物视觉皮质中的神经元特性，旨在通过在源数据域上训练深度学习模型来增强这些模型在目标领域的表现。我们将不使用NeuRN的模型性能作为基准，并与集成NeuRN的模型在图像分类任务上的表现进行比较。 我们在一系列不同的深度学习架构中进行了实验，包括来自神经结构搜索和视觉变换器方法的网络。为了从众多表现出色的深层神经网络中挑选出适合我们实验的模型，我们还提出了一种新颖的方法，利用Needleman-Wunsch算法来计算不同深度学习架构之间的相似性。 我们的研究结果展示了NeuRN的有效性，在跨域图像分类任务上显著改善了基准性能。本框架旨在为未来基于神经科学启发式的深度学习模型奠定基础。
URL
https://arxiv.org/abs/2505.06881
