# ImageNet Pre-training also Transfers Non-Robustness Source: [ImageNet Pre-training also Transfers Non-Robustness]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:50:11.375Z Authors: Jiaming Zhang, Jitao Sang, Qi Yi, Yunfan Yang, Huiwen Dong, Jian Yu Published: 2021-06-21T11:16:13.000Z ## Abstract ImageNet pre-training has enabled state-of-the-art results on many tasks. In spite of its recognized contribution to generalization, we observed in this study that ImageNet pre-training also transfers adversarial non-robustness from pre-trained model into fine-tuned model in the downstream classification tasks. We first conducted experiments on various datasets and network backbones to uncover the adversarial non-robustness in fine-tuned model. Further analysis was conducted on examining the learned knowledge of fine-tuned model and standard model, and revealed that the reason leading to the non-robustness is the non-robust features transferred from ImageNet pre-trained model. Finally, we analyzed the preference for feature learning of the pre-trained model, explored the factors influencing robustness, and introduced a simple robust ImageNet pre-training solution. Our code is available at \\url{https://github.com/jiamingzhang94/ImageNet-Pretraining-transfers-non-robustness}. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()