One of the biggest problems in deep learning is its difficulty to retain consistent robustness when transferring the model trained on one dataset to another dataset. To conquer the problem, deep transfer learning was implemented to execute various vision tasks by using a pre-trained deep model in a diverse dataset. However, the robustness was often far from state-of-the-art. We propose a collaborative weight-based classification method for deep transfer learning (DeepCWC). The method performs the L2-norm based collaborative representation on the original images, as well as the deep features extracted by pre-trained deep models. Two distance vectors will be obtained based on the two representation coefficients, and then fused together via the collaborative weight. The two feature sets show a complementary character, and the original images provide information compensating the missed part in the transferred deep model. A series of experiments conducted on both small and large vision datasets demonstrated the robustness of the proposed DeepCWC in both face recognition and object recognition tasks.
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---8x deeper than VGG nets 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.