A Cycle-Consistency Constrained Framework for Dynamic Solution Space Reduction in Noninjective Regression
This addresses the problem of reducing manual intervention and distribution assumptions in non-injective regression for machine learning practitioners, though it appears incremental as it builds on cycle consistency ideas.
The paper tackled the challenge of multi-output models relying on preset distributions and prior knowledge in non-injective regression by proposing a cycle consistency-based training framework, which achieved a cycle reconstruction error below 0.003 and improved evaluation metrics by about 30% compared to baselines.
To address the challenges posed by the heavy reliance of multi-output models on preset probability distributions and embedded prior knowledge in non-injective regression tasks, this paper proposes a cycle consistency-based data-driven training framework. The method jointly optimizes a forward model Φ: X to Y and a backward model Ψ: Y to X, where the cycle consistency loss is defined as L _cycleb equal L(Y reduce Φ(Ψ(Y))) (and vice versa). By minimizing this loss, the framework establishes a closed-loop mechanism integrating generation and validation phases, eliminating the need for manual rule design or prior distribution assumptions. Experiments on normalized synthetic and simulated datasets demonstrate that the proposed method achieves a cycle reconstruction error below 0.003, achieving an improvement of approximately 30% in evaluation metrics compared to baseline models without cycle consistency. Furthermore, the framework supports unsupervised learning and significantly reduces reliance on manual intervention, demonstrating potential advantages in non-injective regression tasks.