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 layers8x 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.
The paper introduces deep residual networks (ResNets) to address the degradation problem in deep neural networks, where performance worsens with increased network depth due to optimization difficulties.
ResNets use a residual learning framework where each network layer approximates a residual function, facilitated by shortcut connections that perform identity mapping to bypass layers without adding computational complexity.
Experimental results demonstrate the superior performance of ResNets on tasks such as ImageNet and CIFAR-10 classification, and object detection, achieving state-of-the-art error rates and mean Average Precision (mAP) scores.
The paper "Deep Residual Learning for Image Recognition" authored by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, introduces a novel framework for training very deep neural networks, referred to as deep residual networks (ResNets). This work was primarily motivated by the degradation problem which occurs when the depth of a network increases: deeper networks often perform worse during both training and validation, a phenomenon not attributed to overfitting but instead to difficulties in optimization.
The paper's central contribution is the residual learning framework. Traditional network layers aim to approximate a desired function directly, whereas residual networks reformulate this process. Each layer in a residual network approximates a residual function $\mathcal{F}(x) = \mathcal{H}(x) - x$, where $\mathcal{H}(x)$ denotes the desired function, and $x$ is the layer input. Hence, the network learns the residual mapping $\mathcal{F}(x) + x$.
To facilitate residual learning, the authors utilize shortcut connections that perform identity mapping, allowing information to bypass one or more layers. These shortcut connections add neither additional parameters nor computational complexity, ensuring the networks remain efficient.
The proposed ResNets demonstrate substantial performance improvements over plain networks. Specifically, an ensemble of residual nets achieves a top-5 error rate of 3.57\% on the ImageNet test set, surpassing the performance of deep networks like VGG-16 and Inception modules. The study showcases the importance of network depth by evaluating architectures up to 152 layers deep. For instance, a 152-layer ResNet achieves a top-5 error rate of 4.49% on the ImageNet validation set.
On the CIFAR-10 dataset, ResNets outperform their plain counterparts even when composed of over 1000 layers. For example, a 110-layer ResNet achieves a test error of 6.43%, highlighting the potential of extremely deep networks to maintain superior performance. The study also observes that residual functions generally have smaller responses compared to non-residual functions, supporting the framework's effectiveness.
Residual networks also demonstrate exhaustive improvements in object detection and localization tasks. A ResNet-101 model trained on the MS COCO dataset improves the mean Average Precision (mAP) by 6.0% over VGG-16. Additionally, the authors integrated the ResNet into the Faster R-CNN framework and achieved mAPs of up to 63.6% on the ImageNet detection task.
The introduction of the residual learning framework bridges the gap caused by optimization difficulties in deep networks. By alleviating the degradation problem, it establishes a more robust method for training very deep architectures. This reformulation also opens avenues for further theoretical exploration of network optimization techniques.
Practically, the residual networks achieve state-of-the-art results across various benchmarks and tasks, underscoring the power of depth in neural networks. The simplicity of implementing shortcut connections allows for straightforward integration into existing architectures, enhancing their performance without significant overhead.
Given the substantial gains shown by the residual learning framework, future developments in AI might continue to explore deeper network architectures across diverse applications. Alongside, advancements in regularization techniques and optimization strategies will likely build upon this foundation to further mitigate issues arising from training very deep networks. Additionally, extending the principles of residual learning to non-vision tasks can potentially revolutionize areas such as natural language processing and speech recognition.
In conclusion, the paper establishes the residual learning framework as a pivotal development in the field of deep learning, providing a robust solution to the optimization difficulties in very deep networks and setting a new standard for image recognition and beyond.