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, per-channel scale, or both (FiLM-style). These parameters are applied 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)
  

• If both scale and bias are used, the processor outputs 2 * out_channels, split into γ and β.

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❓ Does the context affect each spatial location differently?

No — the same per-channel parameters (scale and/or bias) are 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?

Yes. ContextualConv supports FiLM-style modulation (y = γ(c) * conv(x) + β(c)) and is a lightweight alternative to SE blocks or conditional normalization.

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❓ Is it compatible with grouped/depthwise convolutions?

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