Task-Directed Residual AddUNet:Perfect-Reconstruction Routing for Full-Rate Representations
Paper recasts additive U-Net skip structure as a perfect-reconstruction filter bank and adds full-rate residual routing for task-directed representations.
The work proves a constrained additive U-Net's survivor-skip structure is exactly equivalent to a critically sampled perfect-reconstruction filter bank, and removes complementary-subband restrictions via a full-rate formulation. A Residual Full-Rate PR architecture routes task-irrelevant or redundant structure away from the task pathway while guaranteeing exact reconstruction without invertible operators, a matched synthesis bank, or a learned decoder. On TIMIT, the front-end improves test PER from 28.60±2.09% to 25.76±0.41% with recognizer and training held fixed.
- AddUNet skip structure shown equivalent to critically sampled PR filter bank
- Full-rate formulation guarantees exact reconstruction without invertibility or decoder
- TIMIT PER improved from 28.60% to 25.76% with fixed recognizer
- Identity-shortcut ResNet identified as a full-rate PR system
Full article197 words · extracted from arxiv.org · click to collapse
This paper establishes a perfect-reconstruction (PR) interpretation of AddUNet and its full-rate realization, and introduces a Residual Full-Rate PR architecture for task-directed representation learning. The survivor--skip structure of a constrained additive U-Net is shown to be exactly equivalent to a critically sampled multirate PR filter bank. The full-rate formulation removes the complementary-subband restrictions of the critically sampled system while preserving PR. A Residual Full-Rate PR architecture is then proposed to progressively route task-irrelevant, nuisance, or redundant structure away from the task-facing survivor while retaining the routed information explicitly. Exact reconstruction is guaranteed for arbitrary shape-compatible linear or nonlinear routing operators, without requiring invertibility, a matched synthesis bank, reconstruction loss, or learned decoder. The resulting architecture decouples representation design from reconstruction design: conservation is structural, while learning is devoted to task-directed routing. The same formulation identifies an identity-shortcut ResNet with its residual output retained as a full-rate PR system. Experiments verify exact single-channel routing of linearly separable factors to machine precision. On TIMIT, the proposed front-end improves test PER from $28.60\pm2.09\%$ to $25.76\pm0.41\%$ with the recognizer and training protocol held fixed, while maintaining exact reconstruction. Speaker probing further shows that structural conservation does not itself imply task-specific invariance.
Text extracted automatically; images, tables and formatting may be missing. Original: https://arxiv.org/abs/2609.15857