How do Vision Transformers compare to CNNs in terms of performance on image classification tasks? Pending Review
Asked on Nov 20, 2025
Answer
Vision Transformers (ViTs) and Convolutional Neural Networks (CNNs) are both popular architectures for image classification tasks, but they differ significantly in their approach and performance characteristics. ViTs have shown competitive performance with CNNs, especially on large datasets, by leveraging self-attention mechanisms to capture global image context.
Example Concept: Vision Transformers (ViTs) use a self-attention mechanism to process images by dividing them into patches and treating each patch as a token, similar to words in NLP tasks. This allows ViTs to capture long-range dependencies across an image. In contrast, CNNs use convolutional layers to detect local patterns and features, building up to more complex representations through hierarchical layers. ViTs can outperform CNNs on large datasets due to their ability to model global context, but they may require more data and computational resources to train effectively.
Additional Comment:
- ViTs are generally more data-hungry and require larger datasets to achieve optimal performance compared to CNNs.
- CNNs are traditionally more efficient on smaller datasets due to their inductive biases like locality and translation invariance.
- ViTs can be more computationally intensive due to the self-attention mechanism, which scales quadratically with the number of patches.
- Recent advancements in ViTs include hybrid models that incorporate convolutional layers to improve performance on smaller datasets.
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