Mindeye2: References You Should Check Out

Table of Links

Abstract and 1 Introduction

2 MindEye2 and 2.1 Shared-Subject Functional Alignment

2.2 Backbone, Diffusion Prior, & Submodules

2.3 Image Captioning and 2.4 Fine-tuning Stable Diffusion XL for unCLIP

2.5 Model Inference

3 Results and 3.1 fMRI-to-Image Reconstruction

3.2 Image Captioning

3.3 Image/Brain Retrieval and 3.4 Brain Correlation

3.5 Ablations

4 Related Work

5 Conclusion

6 Acknowledgements and References

A Appendix

A.1 Author Contributions

A.2 Additional Dataset Information

A.3 MindEye2 (not pretrained) vs. MindEye1

A.4 Reconstruction Evaluations Across Varying Amounts of Training Data

A.5 Single-Subject Evaluations

A.6 UnCLIP Evaluation

A.7 OpenCLIP BigG to CLIP L Conversion

A.8 COCO Retrieval

A.9 Reconstruction Evaluations: Additional Information

A.10 Pretraining with Less Subjects

A.11 UMAP Dimensionality Reduction

A.12 ROI-Optimized Stimuli

A.13 Human Preference Experiments

6 Acknowledgements

Special thanks to Dustin Podell, Vikram Voleti, Andreas Blattmann, and Robin Rombach for technical assistance fine-tuning Stable Diffusion XL to support our unCLIP usecase. Thanks to the MedARC Discord community for being the public forum from which this research was developed, particularly thank you to Connor Lane, Alex Nguyen, Atmadeep Bannerjee, Amir Refaee, and Mohammed Baharoon for their helpful discussions. Thanks to Alessandro Gifford and Connor Lane for providing useful feedback on drafts of the manuscript. Thank you to Richard Vencu for help navigating the Stability AI HPC. Thanks to Stability AI for their support for open neuroAI research and providing the computational resources necessary to develop MindEye2. Collection of the Natural Scenes Dataset was supported by NSF IIS-1822683 and NSF IIS-1822929.

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