(x) y = self.model(x) return y class LabelPredictor(nn.Layer): def __init__(self, in_features, num_classes=10): super(LabelPredictor, self).__init__() self.layer = nn.Sequential( nn.Linear(in_features, 512), nn.ReLU(), nn.Linear(512, 512), nn.ReLU(), nn.Linear(512, ...
Transfer learningIn this work, we study the transfer learning problem under high-dimensional generalized linear models (GLMs), which aim to improve the fit on target data by borrowing information from useful source data. Given which sources to transfer, we propose a transfer learning algorithm on ...
1. 前言 迁移学习（Transfer Learning,TL）对于人类来说，就是掌握举一反三的学习能力。比如我们学会骑...
简单来说，预训练模型(pre-trained model)是前人为了解决类似问题所创造出来的模型。你在解决问题的时候...
This paper studies transfer learning of a high-dimensional generalized linear model with the target model as well as source data from different but possibly related models. Both known and unknown transferable domain settings are considered. On the one hand, an improved two-step transfer learning alg...
for name, param in transfer_model.named_parameters(): if("bn" not in name): param.requires_grad = False Then we need to replace the final classification block with a new one that we will train for detecting cats or fish. In this example, we replace it with a couple of Linear layers...
在训练深度学习模型时,有时候我们没有海量的训练样本,只有少数的训练样本(比如几百个图片),几百个训练样本显然对于深度学习远远不够。这时候,我们可以使用别人预训练好的网络模型权重,在此基础上进行训练,这就引入了一个概念——迁移学习(Transfer Learning)。
迁移学习 (Transfer Learning) 是把已学训练好的模型参数用作新训练模型的起始参数。迁移学习是深度学习中非常重要和常用的⼀个策略。 下面是一个简单的 PyTorch 迁移学习示例代码,用于将训练好的 ResNet18 模型应用于 CIFAR-10 数据集的分类任务中:
Cross-stitch Networks for Multi-task Learning - 这篇文章就直接暴力了测试了所有分叉的可能性,并指出不同的任务在不同的分叉上效果。后续他们提出了一个叫 Cross-Stitch Network 的网络结构,通过矩阵中 linear combination 的方式来融合不...
● Learning rates. It’s common to use a smaller learning rate for ConvNet weights that are being fine-tuned, in comparison to the (randomly-initialized) weights for the new linear classifier that computes the class scores of your new dataset. This is because we expect that the ConvNet wei...