The Label Imitation Game: Turing Test Network for Zero-Shot Pseudo-Label Pruning

arXiv cs.CV1mo4 min read

arXiv:2606.30875v1 Announce Type: new Abstract: Foundation model pseudo-labeling - labeling data strictly via zero-shot inference - enables massive scale, but performance is undermined by hallucinations that evade standard thresholds. To eliminate these errors, we introduce the Turing-inspired Label Imitation Game (LIG), a framework that formalizes pseudo-label pruning as an adversarial interrogation. Rather than filtering labels via isolated thresholds, we use the LIG to train a Turing Test Network (TTN), a task-agnostic "judge" that evaluates candidate pseudo-labels within a dataset-wide con

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The Label Imitation Game: Turing Test Network for Zero-Shot Pseudo-Label Pruning

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