StableMTL repurposes latent diffusion models for multi-task learning from partially annotated synthetic data via unified latent loss, task encoding, and a multi-stream task-attention architecture, reporting outperformance on 7 tasks across 8 benchmarks.
Proceedings of the IEEE (2024)
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StableMTL: Repurposing Latent Diffusion Models for Multi-Task Learning from Partially Annotated Synthetic Datasets
StableMTL repurposes latent diffusion models for multi-task learning from partially annotated synthetic data via unified latent loss, task encoding, and a multi-stream task-attention architecture, reporting outperformance on 7 tasks across 8 benchmarks.