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Joint-Output On-Policy Distillation (JO-OPD) is proposed, which distills the model's stronger S2T policy into joint generation using student-generated S2TS trajectories, which reduces OMG across two interleaved generation architectures.
Learning-state-aware dynamic generative data augmentation (LSADA) is proposed, which introduces a decoupled data augmentation and diffusion fusion strategy that applies strength-controlled transformations to class-relevant regions and generates diverse class-irrelevant regions, progressively fusing them to improve imag...
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