To tackle the scarcity of labeled graph data, graph self-supervised learning (SSL) has branched into two paradigms: Generative methods and Contrastive methods. Inspired by MAE and BERT in computer vision (CV) and natural language processing (NLP), masked graph autoencoders (MGAEs) are gaining ...
SPMGAE: Self-purified masked graph autoencoders release robust expression power Neurocomputing Volume 611,1 January 2025, Page 128631 Purchase options CorporateFor R&D professionals working in corporate organizations. Academic and personalFor academic or personal use only. ...
Yuzhi Wang et al. [27] proposed that addressed the challenges posed by large amounts of labelled data by combining a convolutional block attention module (CBAM) with a masked autoencoder (MAE). The model's performance was validated using both the collected dataset and the CCMT dataset. Accura...
This repository is the official implementation of“Denoising Masked Autoencoders Help Robust Classification”, based on the official implementation ofMAEinPyTorch. @inproceedings{wu2023dmae, title={Denoising Masked Autoencoders Help Robust Classification}, author={Wu, QuanLin and Ye, Hang and Gu, Yun...
* Test-Time Training with Masked Autoencoders* 链接: arxiv.org/abs/2209.0752* 作者: Yossi Gandelsman,Yu Sun,Xinlei Chen,Alexei A. Efros* 其他: Project page: this https URL* 摘要: 测试时间培训通过使用自学意义的每个测试输入优化模型,可以随时适应新的测试分布。在本文中,我们将蒙版的自动编码器用...
et al. ConvNeXt V2: Co-designing and scaling ConvNets with masked autoencoders. (2023). 38. Mehta, S. & Rastegari, M. Separable self-attention for mobile vision transformers. (2022). 39. Tschandl, P., Rosendahl, C. & Kittler, H. The HAM10000 dataset, a large collection of multi...
Diverse methods have been proposed for self-supervised 3D object retrieval. Pioneering works use auto-encoder setups in which neural networks are trained to reconstruct (a derivative of) the input. Leng et al.[64]apply convolutional auto-encoders, trained in a layer-wise manner, to 2D depth ...
Paper tables with annotated results for Occlusion-Robust FAU Recognition by Mining Latent Space of Masked Autoencoders
we propose a novel perspective of augmentation to regularize the training process. Inspired by the recent success of applying masked image modeling to self-supervised learning, we adopt the self-supervised masked autoencoder to generate the distorted view of the input images. We show that utilizing...
This repository is the official implementation of “Denoising Masked Autoencoders Help Robust Classification”, based on the official implementation of MAE in PyTorch. @inproceedings{wu2023dmae, title={Denoising Masked Autoencoders Help Robust Classification}, author={Wu, QuanLin and Ye, Hang and Gu...