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Vision Transformers (ViT) Explained: Are They Better Than CNNs?
- Vision Transformers (ViT) aim to implement image classification by applying self-attention globally using the Transformer encoder architecture, outperforming conventional CNNs like ResNet with less computational resources.
- Transformers, initially introduced by Vaswani et al., operate on sequential data like text through self-attention, weighing the importance of each element relative to others, simplifying complex tasks in NLP.
- Self-attention mechanism in transformers captures relationships within a sequence, aiding tasks like translation through contextual understanding of words.
- A transformer transforms input using encoder and decoder blocks with self-attention; the encoder block includes key (K), query (Q), and value (V) sets derived via trainable weight matrices.
- Multi-headed self-attention in transformers enhances performance by allowing different heads to learn distinct relationships, boosting contextual understanding and dealing with variations.
- Vision Transformers process images as sequences of tokens, not pixels, reducing attention map dimension while handling varied image sizes without drastic architectural changes.
- ViT incorporates linear embeddings, positional encodings, and a learnable classifier to preprocess image patches before inputting them into the Transformer encoder for classification.
- ViT models pre-trained on JFT-300M dataset outperform ResNet-based models across datasets, demonstrating superior performance with less pre-training computational resources.
- ViT processing performance excels when pre-trained on extensive datasets but may lack inductive biases present in CNNs on smaller datasets.
- Further advancements in ViT technology could involve scaling for tasks like image detection, segmenting, and developing smaller, more efficient ViT architectures.
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