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Decoder Network

The decoder maps classification spikes back to pixel space using transposed convolutions with tied weights from the encoder and max unpooling with saved indices [2].

Data Flow​

Tied Weights​

Each transposed convolution reuses the transposed kernel from the corresponding encoder layer. This means the decoder has no additional learnable parameters -- it is fully determined by the encoder.

decoder = SpikeSEGDecoder.from_encoder(encoder)

Unpooling​

Max unpooling places each value back at the position of the original max-pooling winner using the stored indices. All other positions are filled with zeros, preserving spatial precision.

Delay Connections​

An optional DelayConnection module maintains spike continuity across timesteps by buffering previous activations with exponential decay. This is controlled by DecoderConfig.use_delay_connections (default: True).

Single-Spike Decoding​

The decoder can also decode a single classification spike at a given spatial location (used by HULK):

mask = decoder.decode_single_spike(
    spike_location=(y, x),
    class_id=0,
    batch_size=1,
    class_spatial_shape=(H_cls, W_cls),
    pool1_indices=...,
    pool2_indices=...,
    pool1_output_size=...,
    pool2_output_size=...,
)

See HULK-SMASH for how this enables instance segmentation.