Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

原文地址:Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning


  1. Inception-v4
  2. Inception-ResNet-v1
  3. Inception-ResNet-v2



Very deep convolutional networks have been central to the largest advances in image recognition performance in recent years. One example is the Inception architecture that has been shown to achieve very good performance at relatively low computational cost. Recently, the introduction of residual connections in conjunction with a more traditional architecture has yielded state-of-the-art performance in the 2015 ILSVRC challenge; its performance was similar to the latest generation Inception-v3 network. This raises the question of whether there are any benefit in combining the Inception architecture with residual connections. Here we give clear empirical evidence that training with residual connections accelerates the training of Inception networks significantly. There is also some evidence of residual Inception networks outperforming similarly expensive Inception networks without residual connections by a thin margin. We also present several new streamlined architectures for both residual and non-residual Inception networks. These variations improve the single-frame recognition performance on the ILSVRC 2012 classification task significantly. We further demonstrate how proper activation scaling stabilizes the training of very wide residual Inception networks. With an ensemble of three residual and one Inception-v4, we achieve 3.08% top-5 error on the test set of the ImageNet classification (CLS) challenge.

极深卷积网络是近年来图像识别性能最大进步的核心。一个例子是Inception架构,它已经被证明在相对较低的计算成本下实现了非常好的性能。最近,在2015年ILSVRC挑战赛中,残差连接与更传统的架构相结合产生了最先进的性能;其性能类似于最新一代的Inception-v3网络。这就提出了这样一个问题:将Inception架构与残差连接相结合是否有效。在这里,我们给出了明确的经验证据表明,使用残差连接的训练显著加快了Inception网络的训练。同样有证据表明,在没有残差连接的情况下,残差Inception网络的性能比Inception网络的性能要差得多。我们还为残差和非残差Inception网络提出了几种新的简化架构。这些变化显著提高了ILSVRC 2012分类任务的单帧识别性能。我们进一步证明了适当的激活尺度如何稳定极宽残差Inception网络的训练。通过集成三个残差和一个Inception-v4网络,我们在ImageNet分类(CLS)挑战的测试集上获得了3.08%的top-5误差率


  • Inception-ResNet-v1:混合Inception版本,其计算成本类似于Inception-v3
  • Inception-ResNet-v2:计算成本更高的混合Inception版本,其识别性能显著提高
  • Inception-v4:纯Inception版本,没有残差连接的情况下识别性能与Inception-ResNet-v2大致相同


Also we found that if the number of filters exceeded 1000, the residual variants started to exhibit instabilities and the network has just “died” early in the training, meaning that the last layer before the average pooling started to produce only zeros after a few tens of thousands of iterations. This could not be prevented, neither by lowering the learning rate, nor by adding an extra batch-normalization to this layer.


We found that scaling down the residuals before adding them to the previous layer activation seemed to stabilize the training. In general we picked some scaling factors between 0.1 and 0.3 to scale the residuals before their being added to the accumulated layer activations (cf. Figure 20).


A similar instability was observed by He et al. in [5] in the case of very deep residual networks and they suggested a two-phase training where the first “warm-up” phase is done with very low learning rate, followed by a second phase with high learning rata. We found that if the number of filters is very high, then even a very low (0.00001) learning rate is not sufficient to cope with the instabilities and the training with high learning rate had a chance to destroy its effects. We found it much more reliable to just scale the residuals.


Even where the scaling was not strictly necessary, it never seemed to harm the final accuracy, but it helped to stabilize the training.


Figure 20. The general schema for scaling combined Inception-resnet moduels. We expect that the same idea is useful in the general resnet case, where instead of the Inception block an arbitrary subnetwork is used. The scaling block just scales the last linear activations by a suitable constant, typically around 0.1.

图20。缩放Inception-ResNet模块的通用模式。我们期望同样的想法在一般的ResNet情况下是有用的,在这种情况下,使用一个任意的子网来代替Inception模块。Activation Scaling模块仅使用合适的常数进行最后的线性激活,通常在0.1左右


  • 优化器:RMSProp,decay=0.9\(\epsilon=1.0\)
  • 学习率:0.045。每2个阶段进行指数衰减,衰减因子为0.94

共运行了20个相同结构的模型,每一个分别运行在Nvidia Kelper GPU上。完成训练后平均这些模型参数得到最后的模型


2显示了单模型 - 单裁剪测试结果

3显示了单模型 - 10/12裁剪测试结果

4显示了单模型 - 144裁剪测试结果

5显示了模型集成 - 144裁剪测试结果