Hyperdeep Addons | Top

Visualization of the addon activation patterns revealed that the hypernetwork learns to route distinct tasks to distinct addon combinations.

This confirms that the "Top" of the network is the most critical area for modular intervention. hyperdeep addons top

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| Model | Method | Parameters (Trainable) | Accuracy (Avg) | Inference Latency (ms) | | :--- | :--- | :--- | :--- | :--- | | BERT-Large | Full Fine-tuning | 340M | 89.2% | 45 | | BERT-Large | LoRA | 8M | 88.9% | 46 | | BERT-Large | AdapterHub | 12M | 88.5% | 52 | | BERT-Large | HDAT (Ours) | 5M | 89.8% | 28 | Visualization of the addon activation patterns revealed that

Let $x$ be the input to a top layer. In a standard transformer, the output is $y = Wx + b$. In HDAT, the weight matrix $W$ is decomposed. The output becomes: $$ y = W_\textfrozenx + \textAddon(x, z) $$ Where $z$ is the latent code generated by the hypernetwork conditioning on the task context. The "Top" aspect refers to the specific focus on the final $N$ layers where $z$ has the highest variance and impact. This confirms that the "Top" of the network

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