imagenet 的准确率预示了基于固定特征逻辑回归的表现,但是 regularization 的设置也有影响 imagenet 的准确率预示了微调的表现 imagenet 的准确率和随机初始化训练网络的表现有关 Benefits of better models are comparable to specialized methods for transfer learning:这里的具体方法可以是一些针对少样本学习的方法 Imag...
Generally, more accurate models on the "upstream" dataset tend to provide better transfer accuracy "down- stream". In this work, we perform an in-depth investi- gation of this phenomenon in the context of convolutional neural networks (CNNs) trained on the ImageNet dataset, which have bee...
4225 Do Better ImageNet Models Transfer Better? Simon Kornblith (Google)*; Jon Shlens (Google); Quoc Le (Google Brain) Representation Learning Deep Learning Oral 1.2.3 6 92 1213 Learning Video Representations from Correspondence Proposals Xingyu Liu (Stanford University)*; Joon-Young Lee (Adobe...
Finally, we propose using an ImageNet pre-trained model combined with a second round of pre-training using in-domain HRRS images, i.e. domain-adaptive pre-training. The experimental results show that domain-adaptive pre-training results in ...
We find that ImageNet pretrained self-supervised models are more generalisable than their supervised counterparts, scoring up to 10% better on medical classification tasks. The two in-domain pretrained models outperformed other models by over 20% on in-domain tasks, however they suffered significant ...
labels. For our dataset, we only have 2. Which means the last few layers for classification is not useful for us. They can be excluded while loading the model by specifying theinclude_topargument to False, and this applies to other ImageNet models made available inKeras applicationsas well....
所涉模 表 7摇 不同分类模型在 ENABL3S 数据集上的模式识别准确率 Table 7摇 Pattern recognition accuracies of different classification models in ENABL3S dataset 文献 特征维度 特征状态 仅肌电 识别模型 稳态错误率 / % 稳态准确率 / % 整体错误率 / % 整体准确率 / % 42( GM鄄06) 处理 否 PCA...
structure between multiple tasks [6]. A common transfer learning strategy is to use pre-trained networks such as those trained on imagenet [15] and fine-tune them on new tasks. While this approach can significantly improve performance for many visual tasks, ...
In the second step of transfer learning, the feature extraction layers of the neural networks are frozen and only the classifier part, which assigns the labels to the samples, is re-trained, with the dataset of interest. In our scenario, we train a CNN with the help of ImageNet first ...
For the training, the transfer learning approach was used because the set of available data was too small to train an own network. A pre-trained network, based on the ImageNet dataset [27], was taken to support the training work. The output layers were removed, and the network was ...