MINR: Implicit Neural Representations with Masked Image Modelling

Sua Lee; Joonhun Lee; Myungjoo Kang

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arXiv · 2507.22404

MINR: Implicit Neural Representations with Masked Image Modelling

Sua Lee·Joonhun Lee·Myungjoo Kang

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Abstract

Self-supervised learning methods like masked autoencoders (MAE) have shown significant promise in learning robust feature representations, particularly in image reconstruction-based pretraining task. However, their performance is often strongly dependent on the masking strategies used during training and can degrade when applied to out-of-distribution data. To address these limitations, we introduce the masked implicit neural representations (MINR) framework that synergizes implicit neural representations with masked image modeling. MINR learns a continuous function to represent images, enabling more robust and generalizable reconstructions irrespective of masking strategies. Our experiments demonstrate that MINR not only outperforms MAE in in-domain scenarios but also in out-of-distribution settings, while reducing model complexity. The versatility of MINR extends to various self-supervised learning applications, confirming its utility as a robust and efficient alternative to existing frameworks.

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BibTeX RIS

Sua Lee, Joonhun Lee, Myungjoo Kang. 2025-07-30. MINR: Implicit Neural Representations with Masked Image Modelling. https://arxiv.org/abs/2507.22404

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