Cross Attention is a mechanism in transformer models where the attention is applied between different sequences, typically between the output of one layer and the input of another. It allows the model to focus on relevant parts of the input sequence while generating output, making it crucial in tasks like translation, where the decoder attends to the encoder's output to generate accurate translations.
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⌚Time Stamps⌚
00:00 - Plan Of Action
03:11 - What is Cross attention
11:35 - The "HOW" of Cross attention
12:45 - Self Attention vs Cross Attention(Input)
15:02 - Self Attention vs Cross Attention (Processing)
24:18 - Self Attention vs Cross Attention (Output)
29:01 - Cross Attention vs Bahdanau/Luang Attention
32:00 - Use Cases
