MINR: Implicit Neural Representations with Masked Image Modelling

• URL: http://arxiv.org/abs/2507.22404v1
• Date: Wed, 30 Jul 2025 06:12:57 GMT
• Title: MINR: Implicit Neural Representations with Masked Image Modelling
• Authors: Sua Lee, Joonhun Lee, Myungjoo Kang,
• Abstract summary: Masked autoencoders (MAE) have shown significant promise in learning robust feature representations.<n>We introduce the masked implicit neural representations (MINR) framework that synergizes implicit neural representations with masked image modeling.<n>MINR learns a continuous function to represent images, enabling more robust and generalizable reconstructions irrespective of masking strategies.
• Score: 5.330266804358638
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Neural Experts: Mixture of Experts for Implicit Neural Representations [41.395193251292895]
  Implicit neural representations (INRs) have proven effective in various tasks including image, shape, audio, and video reconstruction. We propose a mixture of experts (MoE) implicit neural representation approach that enables learning local piece-wise continuous functions. We show that incorporating a mixture of experts architecture into existing INR formulations provides a boost in speed, accuracy, and memory requirements.
  arXiv  Detail & Related papers  (2024-10-29T01:11:25Z)
• BIM: Block-Wise Self-Supervised Learning with Masked Image Modeling [18.861945284506028]
  Masked image modeling (MIM) aims to extract valuable insights from image patches to enhance the feature extraction capabilities of the underlying deep neural network (DNN)
  arXiv  Detail & Related papers  (2023-11-28T20:42:30Z)
• Self-Supervised Neuron Segmentation with Multi-Agent Reinforcement Learning [53.00683059396803]
  Mask image model (MIM) has been widely used due to its simplicity and effectiveness in recovering original information from masked images. We propose a decision-based MIM that utilizes reinforcement learning (RL) to automatically search for optimal image masking ratio and masking strategy. Our approach has a significant advantage over alternative self-supervised methods on the task of neuron segmentation.
  arXiv  Detail & Related papers  (2023-10-06T10:40:46Z)
• MOCA: Self-supervised Representation Learning by Predicting Masked Online Codebook Assignments [72.6405488990753]
  Self-supervised learning can be used for mitigating the greedy needs of Vision Transformer networks. We propose a single-stage and standalone method, MOCA, which unifies both desired properties. We achieve new state-of-the-art results on low-shot settings and strong experimental results in various evaluation protocols.
  arXiv  Detail & Related papers  (2023-07-18T15:46:20Z)
• DPPMask: Masked Image Modeling with Determinantal Point Processes [49.65141962357528]
  Masked Image Modeling (MIM) has achieved impressive representative performance with the aim of reconstructing randomly masked images. We show that uniformly random masking widely used in previous works unavoidably loses some key objects and changes original semantic information. To address this issue, we augment MIM with a new masking strategy namely the DPPMask. Our method is simple yet effective and requires no extra learnable parameters when implemented within various frameworks.
  arXiv  Detail & Related papers  (2023-03-13T13:40:39Z)
• Exploring The Role of Mean Teachers in Self-supervised Masked Auto-Encoders [64.03000385267339]
  Masked image modeling (MIM) has become a popular strategy for self-supervised learning(SSL) of visual representations with Vision Transformers. We present a simple SSL method, the Reconstruction-Consistent Masked Auto-Encoder (RC-MAE) by adding an EMA teacher to MAE. RC-MAE converges faster and requires less memory usage than state-of-the-art self-distillation methods during pre-training.
  arXiv  Detail & Related papers  (2022-10-05T08:08:55Z)
• MA-RECON: Mask-aware deep-neural-network for robust fast MRI k-space interpolation [3.0821115746307672]
  High-quality reconstruction of MRI images from under-sampled kspace' data is crucial for shortening MRI acquisition times and ensuring superior temporal resolution. This paper introduces MA-RECON', an innovative mask-aware deep neural network (DNN) architecture and associated training method. It implements a tailored training approach that leverages data generated with a variety of under-sampling masks to stimulate the model's generalization of the under-sampled MRI reconstruction problem.
  arXiv  Detail & Related papers  (2022-08-31T15:57:38Z)
• Masked Autoencoders are Robust Data Augmentors [9.819398274610933]
  We propose a novel perspective of augmentation to regularize the training process.<n>Inspired by the recent success of applying masked image modeling to self-supervised learning, we adopt the self-supervised masked autoencoder.<n>We show that utilizing such model-based nonlinear transformation as data augmentation can improve high-level recognition tasks.
  arXiv  Detail & Related papers  (2022-06-10T02:41:48Z)
• Adversarial Masking for Self-Supervised Learning [81.25999058340997]
  Masked image model (MIM) framework for self-supervised learning, ADIOS, is proposed. It simultaneously learns a masking function and an image encoder using an adversarial objective. It consistently improves on state-of-the-art self-supervised learning (SSL) methods on a variety of tasks and datasets.
  arXiv  Detail & Related papers  (2022-01-31T10:23:23Z)
• Mask-based Latent Reconstruction for Reinforcement Learning [58.43247393611453]
  Mask-based Latent Reconstruction (MLR) is proposed to predict the complete state representations in the latent space from the observations with spatially and temporally masked pixels. Extensive experiments show that our MLR significantly improves the sample efficiency in deep reinforcement learning.
  arXiv  Detail & Related papers  (2022-01-28T13:07:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.