ICML 2025

You Always Recognize Me (YARM): Robust Texture Synthesis
Against Multi-View Corruption
Weihang Ran 1 Wei Yuan 2 Yinqiang Zheng 1
Abstract
Damage to imaging systems and complex external
environments often introduce corruptions, which
can impair the performance of deep learning mod-
els pretrained on high-quality image data. Previ-
ous methods have focused on restoring degraded
images or fine-tuning models to adapt to out-
of-distribution data. However, these approaches
struggle with complex, unknown corruptions and
often reduce model accuracy on high-quality data.
Inspired by the use of warning colors and camou-
flage in the real world, we propose designing a
robust appearance that can enhance model recog-
nition of low-quality image data. Furthermore,
we demonstrate that certain universal features in
radiance fields can be applied across objects of the
same class with different geometries. We also ex-
amine the impact of different proxy models on the
transferability of robust appearances. Extensive
experiments demonstrate the effectiveness of our
proposed method, which outperforms existing im-
age restoration and model fine-tuning approaches
across different experimental settings, and retains
effectiveness when transferred to models with dif-
ferent architectures. Code will be available at
https://github.com/SilverRAN/YARM.
1. Introduction
Neural network-based deep learning technologies have
made significant impacts across various domains in mod-
ern society, including facial recognition, autonomous driv-
ing, and 3D reconstruction. However, previous research
has shown that neural network models can yield erroneous
predictions in certain scenarios, such as under adversarial
attacks or image degradation (Hosseini et al., 2017; Dodge
1The University of Tokyo, Japan
2Tohoku University,
Japan. Correspondence to: Yinqiang Zheng <yqzheng@ai.u-
tokyo.ac.jp>.
Proceedings of the 42 nd International Conference on Machine
Learning, Vancouver, Canada. PMLR 267, 2025. Copyright ...