Completed
- Intro & Overview
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Classroom Contents
HyperTransformer - Model Generation for Supervised and Semi-Supervised Few-Shot Learning
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- 1 - Intro & Overview
- 2 - Weight-generation vs Fine-tuning for few-shot learning
- 3 - HyperTransformer model architecture overview
- 4 - Why the self-attention mechanism is useful here
- 5 - Start of Interview
- 6 - Can neural networks even produce weights of other networks?
- 7 - How complex does the computational graph get?
- 8 - Why are transformers particularly good here?
- 9 - What can the attention maps tell us about the algorithm?
- 10 - How could we produce larger weights?
- 11 - Diving into experimental results
- 12 - What questions remain open?
