JigsawGAN: Auxiliary Learning for Solving Jigsaw Puzzles With Generative Adversarial Networks

Area of Science:

• Computer Vision
• Artificial Intelligence
• Machine Learning

Background:

• Traditional jigsaw puzzle solvers rely on piece boundaries, often neglecting crucial semantic information.
• Solving jigsaw puzzles with unpaired images, where initial image knowledge is absent, presents a significant challenge.

Purpose of the Study:

• To develop an advanced method for solving jigsaw puzzles by integrating semantic understanding.
• To propose JigsawGAN, a Generative Adversarial Network (GAN)-based auxiliary learning framework for jigsaw puzzle solving.

Main Methods:

• A multi-task learning pipeline combining a classification branch for permutation identification and a GAN branch for feature-to-image recovery.
• The classification branch uses pseudo-labels from shuffled pieces, while the GAN branch leverages image semantics.
• A flow-based warp module integrates classification results to correct feature order.

Main Results:

• JigsawGAN demonstrates superior performance in solving jigsaw puzzles compared to existing methods.
• The method efficiently reconstructs images by simultaneously utilizing semantic and boundary information.
• Qualitative and quantitative evaluations confirm the effectiveness of the proposed approach.

Conclusions:

• JigsawGAN offers a more efficient and effective solution for jigsaw puzzle solving by incorporating semantic information.
• The proposed GAN-based auxiliary learning method advances the field of image reconstruction and puzzle solving.