ICLR 2025 submission - CYCLE-CONSISTENT LEARNING FOR JOINT LAYOUT-TO-IMAGE GENERATION AND OBJECT DETECTION

In this episode, we discuss CYCLE-CONSISTENT LEARNING FOR JOINT LAYOUT-TO-IMAGE GENERATION AND OBJECT DETECTION by The paper's authors are listed as "Anonymous authors" since it is under double-blind review.. The paper introduces a new generation-detection cycle consistent (GDCC) learning framework that simultaneously optimizes layout-to-image generation and object detection, highlighting the inherent duality of these tasks. GDCC employs cycle losses to guide both tasks, enhancing data efficiency without requiring paired datasets, and achieves computational efficiency through novel sampling strategies while keeping inference cost unchanged. Experimental results demonstrate that GDCC improves diffusion model controllability and object detector accuracy, with plans for code release.