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A Residual Learning Framework for Advanced Visual Recognition
Issue Vol. 2 No. 01 (2025): Volume 02 Issue 01 --- Section Articles
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Abstract
The advancement of deep learning in computer vision has been largely driven by the development of increasingly deep neural networks. However, a fundamental challenge known as the "degradation problem" has hindered progress, where adding more layers to a suitably deep model leads to higher training error, preventing the network from benefiting from increased depth. This phenomenon is not caused by overfitting but by optimization difficulties inherent in training very deep architectures. To address this, we introduce a deep residual learning framework. Instead of expecting stacked layers to learn an underlying mapping directly, we reformulate them to learn a residual function with respect to the layer inputs. This is achieved by introducing "shortcut connections" that perform identity mapping, adding the input of a block of layers to its output. This reformulation simplifies the optimization process, as it is easier for the network to learn perturbations from an identity mapping than to learn the mapping from scratch.
We provide comprehensive empirical evidence on several benchmark datasets. On the ImageNet 2012 classification dataset, our residual networks (ResNets) are substantially deeper than previous models, with up to 152 layers, yet exhibit lower complexity than shallower networks like VGG. These ResNets easily overcome the degradation problem, showing consistent accuracy gains from increased depth and achieving a 3.57% top-5 error on the ImageNet test set with an ensemble model. We also conducted experiments on the CIFAR-10 dataset, successfully training networks with over 1000 layers, demonstrating that our framework effectively resolves the core optimization issues. Furthermore, the representations learned by our deep residual networks generalize exceptionally well to other computer vision tasks. When used as a backbone for object detection on PASCAL VOC and MS COCO, our models achieve state-of-the-art results, with a 28% relative improvement on the COCO detection metric. Our findings establish deep residual learning as a fundamental and effective technique for training extremely deep neural networks, pushing the boundaries of what is possible in image recognition.
Keywords
References
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