New AI research proposes VanillaNet: a new neural network architecture that emphasizes elegance and simplicity of design while maintaining remarkable performance in computer vision tasks

Artificial neural networks have advanced significantly in recent decades, driven by the idea that greater network complexity translates into better performance. These networks can perform a variety of human-like tasks, including facial recognition, speech recognition, object identification, natural language processing, and content synthesis, which include several layers and many neurons or processing blocks. Modern technology has tremendous processing power, allowing neural networks to perform these tasks excellently and efficiently. As a result, AI-enhanced technology, such as smartphones, AI cameras, voice assistants, and autonomous cars, are increasing in their daily lives.

Undoubtedly, a significant achievement in this area is the creation of AlexNet, a neural network with 12 layers that offers cutting-edge performance in the large-scale image recognition benchmark. ResNet extends this result to include identity mappings via shortcut connections, enabling training of well-performing deep neural networks in various computer vision applications, including image classification, object identification, and semantic segmentation. The representation capabilities of deep neural networks have unquestionably been enhanced by the inclusion of human-designed modules in these models and the continued increase in network complexity, sparking a flurry of research into how to train networks with more complex architectures to achieve even higher performance. higher.

Previous research included transformer topologies for image recognition tasks as well as convolutional structures, showing its potential for using huge amounts of training data. With an outstanding top-1 accuracy of 90.45% on the ImageNet dataset, some have explored the scaling laws of vision transformer topologies. This result shows that deeper transformer architectures, such as convolutional networks, often exhibit better performance. For even greater accuracy, some have also suggested extending the depth of transformers to 1,000 layers. By revisiting the design space for neural networks and introducing ConvNext, we were able to match the performance of state-of-the-art transformer topologies. Deep and complicated neural networks with good optimization can work satisfactorily, but deployment becomes more difficult as complexity increases.

For example, ResNet shortcuts that combine functionality of many layers use off-chip memory traffic significantly. Furthermore, technical implementation, including rewriting CUDA codes, is required for complex operations such as axial shift in AS-MLP and shift window auto-attention in Swin Transformer. These difficulties require a paradigm shift in neural network design towards simplicity. However, neural networks with only convolutional layers (and no add-ons or shortcuts) have been abandoned in favor of ResNet. This is mainly because the performance gain from including convolutional layers fell below expectations. Second, a simple 34-layer network performs worse than an 18-layer one due to gradient disappearance, a problem with simple networks without shortcuts.

Deep and sophisticated networks, including ResNet and ViT, have also significantly outperformed simpler networks such as AlexNet and VGGNet in terms of performance. As a result, the design and optimization of neural networks with basic topologies have received less attention. It would be very beneficial to address this problem and create efficient models. To achieve this, researchers from Huawei Noahs Ark Lab and the University of Sydney suggest VanillaNet, a cutting-edge neural network architecture that emphasizes beauty and simplicity of design, resulting in outstanding performance in computer vision applications. VanillaNet achieves this by avoiding excessive depth, shortcuts, and difficult procedures like self-attention. As a result, several lean networks are created that handle the problem of inherent complexity and are suitable for contexts with limited resources.

They thoroughly examine the problems caused by their reduced projects and develop a thorough training technique to train their suggested VanillaNets. This method starts with several levels that have nonlinear activation functions. They gradually remove these nonlinear layers during training, simplifying joining while maintaining speed of inference. They propose an effective series-based activation function with several learnable affine modifications to increase the non-linearity of the networks. Using these strategies has been shown to significantly improve the performance of less sophisticated neural networks. VanillaNet surpasses modern networks with complex topologies in terms of effectiveness and accuracy, demonstrating the promise of a simple deep learning strategy. By challenging the accepted standards of underlying models and charting a new course for the development of accurate and efficient models, this groundbreaking review of VanillaNet opens the door to a new approach to neural network architecture. The PyTorch implementation is available on GitHub.

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