Adjoint spectral opening for stable pooling
Implementation & benchmark of arXiv:2608.20399 — Morphological Representation Theory in the Fourier Inf-Semilattice: Universal Decomposition of Frequency-Domain Deep Learning Operators
Source paper: Morphological Representation Theory in the Fourier Inf-Semilattice: Universal Decomposition of Frequency-Domain Deep Learning Operators arXiv:2608.20399 ⓘ · analyzed Sep 1, 2026
AI-generated research hypothesis, automatically tested. Not peer-reviewed.
Idea description
Construct a learnable spectral pooling block as an erosion followed by its adjoint dilation, making the resulting opening idempotent, increasing, and anti-extensive. The block can suppress frequencies outside a learned passband while guaranteeing that applying it twice does not continue changing the representation, which is useful in multi-stage CNN pyramids and U-Net skip paths.
Formulas
Mathematical statement
For an adjunction (epsilon, delta), the paper gives epsilon(f) <=_2 g if and only if f <=_1 delta(g). Proposition 2.4 states that epsilon commutes with infima, delta commutes with suprema, epsilon is anti-extensive (epsilon(f) <= f), delta is extensive (g <= delta(g)), and gamma = delta composed with epsilon is an opening: gamma^2 = gamma, gamma(f) <= f, and gamma is increasing. In the adapted finite spectral model, f is a nonnegative frequency magnitude, epsilon_psi is a quotient erosion, and delta_psi is implemented as the order-adjoint supremum map delta_psi(g)(xi) = g(xi)(psi(xi)+epsilon). The discrete pair satisfies epsilon_psi(f) <= g if and only if f <= delta_psi(g) pointwise. Therefore gamma_psi(f) = delta_psi(epsilon_psi(f)) is an idempotent spectral filter. Because arbitrary FFT magnitudes will not necessarily satisfy every continuous-lattice regularity assumption, idempotence and the adjunction should be unit-tested directly on the discretized tensors rather than assumed from the theorem.
Implementation notes
Use this as a pooling or skip-connection operator in a frequency-aware U-Net or CNN. At a chosen resolution, compute X=fft2(x), magnitude f=abs(X), and phase p=X/(f+eps). Parameterize a positive spectral structuring element psi=softplus(theta)+psi_min; initialize theta so psi is large outside the desired passband and near one inside it. Compute the erosion e=f/(psi+eps), then the adjoint dilation d=e*(psi+eps). In exact arithmetic d=f, so a literal same-grid quotient/multiplication pair is only a validity test, not a useful pooling layer. To make the opening nontrivial, insert a low-resolution projection P between them: downsample e to a coarse Fourier grid, optionally apply a nonnegative max/average aggregation, upsample it with U, and set o=U(P(e))*(psi+eps), clipping o to f to retain anti-extensivity. Alternatively define delta as a learned supremum over neighboring frequency bins, then verify the adjunction numerically. Reconstruct y=real(ifft2(o*p)), and use y as the pooled branch or as a filtered skip feature. The code should test ||gamma(gamma(f))-gamma(f)||, gamma(f)<=f, and monotonicity on random nonnegative f. First cheap experiment: compare a standard stride-2 convolution, average-pool-plus-convolution, and this opening block in a small U-Net on CIFAR-10 or 128x128 BSD denoising. Track aliasing under frequencies above the post-stride Nyquist limit, validation accuracy or PSNR, and repeated-application drift. Success means lower high-frequency alias energy and materially smaller representation drift when the opening is applied twice, with no worse accuracy at equal memory.
Verification
This idea has not been verified yet.
Verification happens in two stages: Stage 1 — a mechanism check on a toy system confirms the claimed mathematical phenomenon reproduces; Stage 2 — a benchmark implements the idea on a real (small) neural network task and compares it against a tuned baseline over 8 paired seeds with a permutation test.
Artifacts
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