🤖 AI Summary
This work addresses the inaccuracy in evaluating channel redundancy under extreme structured channel pruning by proposing a novel channel importance criterion based on complex-valued interaction fields and spectral reconstruction fidelity. Specifically, a complex interaction field is constructed between the input and each individual output channel, and a lightweight autoencoder is employed to reconstruct its Fourier spectrum; the resulting reconstruction error serves as a measure of channel compressibility. Combined with an L1-norm thresholding strategy, this approach enables threshold-driven structured pruning. To the best of our knowledge, this is the first method to incorporate complex-valued signal modeling and spectral autoencoders into channel pruning. On VGG16 trained on CIFAR-10, it achieves 90.11% FLOPs reduction and 96.30% parameter compression with only a 1.67% drop in Top-1 accuracy (from 93.44%), substantially outperforming existing extreme pruning methods.
📝 Abstract
We propose Spectral Complex Autoencoder Pruning (SCAP), a reconstruction-based criterion that measures functional redundancy at the level of individual output channels. For each convolutional layer, we construct a complex interaction field by pairing the full multi-channel input activation as the real part with a single output-channel activation (spatially aligned and broadcast across input channels) as the imaginary part. We transform this complex field to the frequency domain and train a low-capacity autoencoder to reconstruct normalized spectra. Channels whose spectra are reconstructed with high fidelity are interpreted as lying close to a low-dimensional manifold captured by the autoencoder and are therefore more compressible; conversely, channels with low fidelity are retained as they encode information that cannot be compactly represented by the learned manifold. This yields an importance score (optionally fused with the filter L1 norm) that supports simple threshold-based pruning and produces a structurally consistent pruned network. On VGG16 trained on CIFAR-10, at a fixed threshold of 0.6, we obtain 90.11% FLOP reduction and 96.30% parameter reduction with an absolute Top-1 accuracy drop of 1.67% from a 93.44% baseline after fine-tuning, demonstrating that spectral reconstruction fidelity of complex interaction fields is an effective proxy for channel-level redundancy under aggressive compression.