从ImageNet开始,我们通过剥离其原始纹理的每一个图像,并通过AdaIN样式转移(Huang&Belongie,2017)将其替换为随机选择的绘画风格,构建了一个新的数据集(称为Stylized-ImageNet或SIN)(参见图3中的示例),其样式化系数= 1.0。我们使用Kaggle的Painter by Numbers数据集作为风格来源,因为它的风格多样和大小(79,434幅画)。
我们假设ImageNet预先训练的模型比从头开始训练的SRNet的数据效率更高——ALASKA I的JPEG质量因子有两倍的图像(ALASKA II的图像质量因子为25,000,ALASKA I的图像质量因子为50,000)。通过正确的设计,ImageNet预训练模型能够在较少的数据下获得更可靠的性能,这似乎与[30]:“4.7的观察结果一致。ImageNet预训练的准确性...
对抗样本(论文解读十二): Imagenet-trained cnns are Biased towards Texture; Increasing Shape Bias Improves,程序员大本营,技术文章内容聚合第一站。
可以通过对训练数据集合适的改动,使得CNN由偏向于纹理学习的模式转变为偏向于形状学习的模式。 最后采取的方案是,同时训练ImageNet和Stylized-ImageNet同时学习形状和纹理特点,可以得到优于单独学习某一个的效果(其中单独学习形状好于纹理,~~这个结果让人有点不解,难道是分类任务的全局性来说形状特征的鲁棒性更强?至少...
ImageNet-trained CNNs are biased towards texture; increasing shape bias阅读笔记,程序员大本营,技术文章内容聚合第一站。
ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness 经过imagenet训练的cnn 下载积分: 500 内容提示: Under review as a conference paper at ICLR 2019I MAGE N ET - TRAINED CNN S ARE BIASED TOWARDSTEXTURE ; INCREASING SHAPE BIAS IMPROVESACCURACY ...
However, the impact of the ImageNet pre-trained classification layer in parameter fine-tuning is mostly unexplored in the literature. In this paper, we propose a fine-tuning approach with the pre-trained classification layer. We employ layer-wise fine-tuning to determine which layers should be ...
Pre-trained models, data, code & materials from the paper "ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness" (ICLR 2019 Oral) - rgeirhos/texture-vs-shape
This repository contains convolutional neural network (CNN) models trained on ImageNet by Marcel Simon at the Computer Vision Group Jena (CVGJ) using the Caffe framework as published in the accompanying technical report. Each model is in a separate subfolder and contains everything needed to ...
我们证明了所产生的语义分割模型可以在不同的计算预算下进行评估, 即使在嵌入式 GPU 平台上也是可行的。我们在不同的道路驾驶数据集上展示了 ImageNet 预培训和从头开始学习的实验。我们的实验在所有现有的实时执行方法中实现了最先进的语义分割精度。 2. Related Work...