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Abstract This paper investigates the application of deep learning to image classification, proposes a network architecture incorporating attention mechanisms, and validates on CIFAR-10. Experiments show a 3.5 percentage point accuracy improvement.
Keywords Deep learning; convolutional neural network; image classification
Image classification is one of the core tasks in computer vision. With the rise of deep learning, related research has achieved significant progress[1], but challenges still exist in fine-grained scenarios[2]。
We use ResNet-50 as the backbone network and introduce an attention module. The overall model can be expressed as
where θ is a learnable parameter, optimized iteratively via backpropagation.
Training for 200 epochs on NVIDIA RTX 4090. Accuracy changes with training iterations as shown in Figure 1, with stable validation set convergence.
Compared to the baseline, our method consistently achieves superior top-1 accuracy, validating the effectiveness of the attention module.
- [1] He K, et al. Deep Residual Learning for Image Recognition. CVPR, 2016.
- [2] Vaswani A, et al. Attention Is All You Need. NeurIPS, 2017.
Abstract: This paper studies deep learning for image classification. We propose a new network architecture and, on the CIFAR-10 dataset, ran experiments that improved accuracy by 3.5 percentage points.
1. Introduction
Image classification is one of the core tasks in computer vision. Recent advances in deep learning have led to significant progress in related research(2023)Need to add more literature review here
2. Method
We employ ResNet-50 as the backbone network and incorporate an attention mechanism. The overall model can be expressed asy=f(x;θ), where θ is a learnable parameter.
3. Experiments
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