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

arXiv: https://arxiv.org/abs/2101.07555 · HTML: https://mlsanity.com/paper/2101.07555/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 42 references that are in the map.
- First posted on arXiv 19 Jan 2021.

## Builds on (most cited first, 15 of 15)

- [Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles](https://mlsanity.com/paper/1603.09246/) (2016)
- [Unsupervised Visual Representation Learning by Context Prediction](https://mlsanity.com/paper/1505.05192/) (2015)
- [Deeper, Broader and Artier Domain Generalization](https://mlsanity.com/paper/1710.03077/) (2017)
- [Pointer Networks](https://mlsanity.com/paper/1506.03134/) (2015)
- [Domain Generalization by Solving Jigsaw Puzzles](https://mlsanity.com/paper/1903.06864/) (2019)
- [Fine-Grained Visual Classification via Progressive Multi-Granularity Training of Jigsaw Patches](https://mlsanity.com/paper/2003.03836/) (2020)
- [Deep Auxiliary Learning for Visual Localization and Odometry](https://mlsanity.com/paper/1803.03642/) (2018)
- [DeepPermNet: Visual Permutation Learning](https://mlsanity.com/paper/1704.02729/) (2017)
- [Deepzzle: Solving Visual Jigsaw Puzzles with Deep Learning andShortest Path Optimization](https://mlsanity.com/paper/2005.12548/) (2020)
- [Solving Jigsaw Puzzles with Eroded Boundaries](https://mlsanity.com/paper/1912.00755/) (2019)
- [Solving Jigsaw Puzzles with Linear Programming](https://mlsanity.com/paper/1511.04472/) (2015)
- [Solving Jigsaw Puzzles By the Graph Connection Laplacian](https://mlsanity.com/paper/1811.03188/) (2018)
- [Image Reassembly Combining Deep Learning and Shortest Path Problem](https://mlsanity.com/paper/1809.00898/) (2018)
- [DNN-Buddies: A Deep Neural Network-Based Estimation Metric for the Jigsaw Puzzle Problem](https://mlsanity.com/paper/1711.08762/) (2017)
- [Jigsaw Puzzle Solving Using Local Feature Co-Occurrences in Deep Neural Networks](https://mlsanity.com/paper/1807.03155/) (2018)

## Built on by (most cited first, 16 of 16)

- [Know Your Self-supervised Learning: A Survey on Image-based Generative and Discriminative Training](https://mlsanity.com/paper/2305.13689/) (2023)
- [DiffAssemble: A Unified Graph-Diffusion Model for 2D and 3D Reassembly](https://mlsanity.com/paper/2402.19302/) (2024)
- [PuzzleFusion: Unleashing the Power of Diffusion Models for Spatial Puzzle Solving](https://mlsanity.com/paper/2211.13785/) (2022)
- [UPHDR-GAN: Generative Adversarial Network for High Dynamic Range Imaging with Unpaired Data](https://mlsanity.com/paper/2102.01850/) (2021)
- [Self-supervised Learning for Sonar Image Classification](https://mlsanity.com/paper/2204.09323/) (2022)
- [Solving Masked Jigsaw Puzzles with Diffusion Vision Transformers](https://mlsanity.com/paper/2404.07292/) (2024)
- [A Survey on Computational Solutions for Reconstructing Complete Objects by Reassembling Their Fractured Parts](https://mlsanity.com/paper/2410.14770/) (2024)
- [Multi-Phase Relaxation Labeling for Square Jigsaw Puzzle Solving](https://mlsanity.com/paper/2303.14793/) (2023)
- [TEN: Twin Embedding Networks for the Jigsaw Puzzle Problem with Eroded Boundaries](https://mlsanity.com/paper/2203.06488/) (2022)
- [PuzLM: Solving Jigsaw Puzzles with Sequence-to-Sequence Language Models](https://mlsanity.com/paper/2511.06315/) (2025)
- [The Missing GAP: From Solving Square Jigsaw Puzzles to Handling Real World Archaeological Fragments](https://mlsanity.com/paper/2605.12077/) (2026)
- [Tazza: Shuffling Neural Network Parameters for Secure and Private Federated Learning](https://mlsanity.com/paper/2412.07454/) (2024)
- [How PARTs assemble into wholes: Learning the relative composition of images](https://mlsanity.com/paper/2506.03682/) (2025)
- [Deep Learning-Enhanced Visual Monitoring in Hazardous Underwater Environments with a Swarm of Micro-Robots](https://mlsanity.com/paper/2503.02752/) (2025)
- [BrainPuzzle: Hybrid Physics and Data-Driven Reconstruction for Transcranial Ultrasound Tomography](https://mlsanity.com/paper/2510.20029/) (2025)
- [JigSync: Gauge-Resolved Synchronization for Jigsaw Reassembly under Unknown Piece Orientation](https://mlsanity.com/paper/2609.18379/) (2026)

## Abstract (arXiv)

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.