Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers—8× deeper than VGG nets [41] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.
Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of net...
Deep convolutional neural networks [22, 21] have led to a series of breakthroughs for image classification [21, 50, 40]....
Residual Representations. In image recognition, VLAD [18] is a representation that encodes by the residual vectors with ...
Let us consider $\mathcal{H}(\mathbf{x})$ as an underlying mapping to be fit by a few stacked layers (not necessarily th...
We adopt residual learning to every few stacked layers. A building block is shown in Fig. 2. Formally, in this paper we ...
We have tested various plain/residual nets, and have observed consistent phenomena. To provide instances for discussion,...
Our implementation for ImageNet follows the practice in [21, 41]. The image is resized with its shorter side randomly sa...
We evaluate our method on the ImageNet 2012 classification dataset [36] that consists of 1000 classes. The models are tr...
We conducted more studies on the CIFAR-10 dataset [20], which consists of 50k training images and 10k testing images in ...
Our method has good generalization performance on other recognition tasks. Table 7 and 8 show the object detection basel...
In this section we introduce our detection method based on the baseline Faster R-CNN [32] system. The models are initial...
For completeness, we report the improvements made for the competitions. These improvements are based on deep features an...
The ImageNet Localization (LOC) task [36] requires to classify and localize the objects. Following [40, 41], we assume t...