Convolutional neural networks (CNNs) have demonstrated remarkable success in vision-related tasks. However, their susceptibility to failing when inputs deviate from the training distribution is well-documented. Recent studies suggest that CNNs exhibit a bias toward texture instead of object shape in image classification tasks, and that background information may affect predictions. This paper investigates the ability of CNNs to adapt to different color distributions in an image while maintaining context and background. The results of our experiments on modified MNIST and FashionMNIST data demonstrate that changes in color can substantially affect classification accuracy. The paper explores the effects of various regularization techniques on generalization error across datasets and proposes a minor architectural modification utilizing the dropout regularization in a novel way that enhances model reliance on color-invariant intensity-based features for improved classification accuracy. Overall, this work contributes to ongoing efforts to understand the limitations and challenges of CNNs in image classification tasks and offers potential solutions to enhance their performance.

翻译：卷积神经网络(CNN)在视觉任务中取得了显著成功。然而，已有充分文献记载其在输入数据偏离训练分布时容易失效。最新研究表明，CNN在图像分类任务中倾向于依赖纹理而非物体形状，且背景信息可能影响预测结果。本文探究了CNN在保持上下文与背景信息的前提下，适应图像中不同颜色分布的能力。针对改进版MNIST和FashionMNIST数据集的实验结果表明，颜色变化会显著影响分类准确率。本文分析了多种正则化技术对跨数据集泛化误差的影响，并提出了一种微小的架构改进：通过创新性应用Dropout正则化，增强模型对颜色不变性强度特征的依赖，从而提升分类准确率。总体而言，本研究为理解CNN在图像分类任务中的局限性与挑战提供了新见解，并提出了改进其性能的潜在解决方案。