FAQ

❓ What is the context vector used for?

The context vector is a global input (e.g., class label, latent code) that is transformed into a per-channel bias. This bias is added uniformly across all spatial (Conv2d) or temporal (Conv1d) positions in the output.

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❓ How is the context processed?

The context is passed through a ContextProcessor:

• If h_dim is not set → a single linear layer:

  Linear(context_dim, out_channels)
  

• If h_dim is set → a two-layer MLP with ReLU:

  Linear(context_dim, h_dim) → ReLU → Linear(h_dim, out_channels)
  

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❓ Does the context affect each spatial location differently?

No — the same per-channel bias is broadcast across all positions. The context acts globally.

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❓ What happens if I don’t provide context_dim?

The layer behaves exactly like a standard nn.Conv1d or nn.Conv2d.

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❓ Can this replace SE blocks or FiLM?

In spirit, yes. While it’s not spatially aware like attention, the design enables global conditioning and is similar to SE blocks (in additive form).

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❓ Is it compatible with grouped/depthwise convolutions?

Yes. The same context bias is shared across groups, respecting the weight-sharing behavior of grouped convolutions.