P2CL: Prototype-Constrained Consistent Learning — Toward Controllable and Consistent Transfer

Partial domain adaptation remains challenging when label spaces are asymmetric: private source classes dominate alignment, while adaptation signals are miscalibrated across different model views. We argue two under-explored factors jointly drive failure: (i) insufficient controllability over transfer selection under label asymmetry, and (ii) miscalibration between geometric similarity and classifier logits, yielding decision conflicts and noisy-target amplification. In this paper, we present P2CL, a Prototype-constrained consistent Learning framework with Controllable Prototype-gated Transfer to decide what and how to align, prioritizing highly transferable samples and suppressing private-class interference. To reduce decision conflicts, we introduce Discrepancy-gated Consistency Calibration, a lightweight mechanism aligning geometric cues with classifier logits via discrepancy-aware consistency constraints, suppressing noise amplification. Extensive experiments across three benchmarks show consistent gains over recent strong approaches and clearer shared-class separation in adaptation.