deconvolution networks

**Deconvolution Networks** (DeconvNets) are a **visualization technique that projects feature activations back to the input pixel space** — using an approximate inverse of the convolutional network to reconstruct what input pattern caused a particular neuron or feature map activation. **How DeconvNets Work** - **Forward Pass**: Run the input through the CNN, record activations at the layer of interest. - **Set Target**: Zero out all activations except the neuron(s) to visualize. - **Backward Projection**: Pass through "deconvolution" layers — transpose conv, unpooling (using switch positions), ReLU. - **ReLU Handling**: Apply ReLU in the backward pass based on the sign of the backward signal (not the forward activation). **Why It Matters** - **Feature Understanding**: Visualize what each neuron in the CNN has learned to detect. - **Debugging**: Identify neurons that detect artifacts, noise, or irrelevant features. - **Historical**: Zeiler & Fergus (2014) — one of the first systematic approaches to understanding CNN features. **DeconvNets** are **the CNN's projector** — projecting internal feature activations back to pixel space to reveal what patterns each neuron detects.