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GhostNet introduces a novel approach to neural network lightweighting by rethinking how feature maps are generated—replacing expensive convolutions with structured, low-cost operations. Its evolution from V1 to V2 integrates spatial attention in a hardware-friendly manner, enabling stronger representational power without compromising inference ...

Understanding Vision Transformers for Image Classification The Vision Transformer (ViT) represents a groundbreaking approach that merges principles from natural language processing with computer vision. This architecture leverages self-attention mechanisms to achieve impressive results in image classification tasks without relying on traditiona ...

Attention Mechanisms Multi-Head Attention The attention mechanism computes: The scaling factor \(\sqrt{d_k}\) prevents large inner product values that could cause gradient instability. Assuming Q and K elements have mean 0 and variance \(\sigma^2\), the variance of \(QK^T\) grows with \(d_k\). Scaling by \(\sqrt{d_k}\) maintains stable varianc ...

Attention Mechanisms and Transformers The attention mechanism addresses a fundamental challenge in deep learning: transforming variable-dimensional inputs into fixed-dimensional outputs through a weighted aggregation process. This capability proves essential when dealing with sequences or sets of varying sizes, where traditional fixed-parameter ...