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

TL;DR

JigsawGAN introduces a GAN-based auxiliary learning framework that leverages semantic and boundary information to efficiently solve jigsaw puzzles without prior image knowledge.

Contribution

The paper presents a novel multi-task GAN approach combining permutation classification and image recovery for unpaired jigsaw puzzle solving.

Findings

Outperforms existing methods in accuracy and efficiency.

Effectively utilizes semantic information for puzzle solving.

Demonstrates superior qualitative and quantitative results.

Abstract

The paper proposes a solution based on Generative Adversarial Network (GAN) for solving jigsaw puzzles. The problem assumes that an image is divided into equal square pieces, and asks to recover the image according to information provided by the pieces. Conventional jigsaw puzzle solvers often determine the relationships based on the boundaries of pieces, which ignore the important semantic information. In this paper, we propose JigsawGAN, a GAN-based auxiliary learning method for solving jigsaw puzzles with unpaired images (with no prior knowledge of the initial images). We design a multi-task pipeline that includes, (1) a classification branch to classify jigsaw permutations, and (2) a GAN branch to recover features to images in correct orders. The classification branch is constrained by the pseudo-labels generated according to the shuffled pieces. The GAN branch concentrates on the image semantic information, where the generator produces the natural images to fool the discriminator, while the discriminator distinguishes whether a given image belongs to the synthesized or the real target domain. These two branches are connected by a flow-based warp module that is applied to warp features to correct the order according to the classification results. The proposed method can solve jigsaw puzzles more efficiently by utilizing both semantic information and boundary information simultaneously. Qualitative and quantitative comparisons against several representative jigsaw puzzle solvers demonstrate the superiority of our method.