Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters 🔍About| 🚀Quick Start| 📊Evaluation| 🔗Citation 🔥 NEWS: VisionTS achieved the#1rank 🏆 for zero-shot point forecasting (MASE)
Design a patches masked autoencoder by CNN. Contribute to JJLi0427/CNN_Masked_Autoencoder development by creating an account on GitHub.
Masked autoencodersInternal representation connectionsTime series self-supervised methods have been widely used, with electrocardiogram (ECG) classification tasks also reaping their benefits. One mainstream paradigm is masked data modeling, which leverages the visible part of data to reconstruct the masked ...
Enhancing the expressive capacity of deep learning-based time series models with self-supervised pre-training has become ever-increasingly prevalent in time series classification. Even though numerous efforts have been devoted to developing self-supervised models for time series data, we argue that the...
只要你不是与世隔绝的深度炼丹者,应该都知道前阵子恺明大神的佳作 MAE(Masked Autoencoders Are Scalable Vision Learners),自双11那天挂到 arXiv 后,江湖上就开始大肆吹捧:'yyds'、'best paper 预定' 什么的满天飞.. 造成这一现象最主要原因还是大神本身的光环所致,另外就是大家看到 paper 中展示的 mask 掉图...
ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders Sanghyun Woo1* Shoubhik Debnath2 Ronghang Hu2 Xinlei Chen2 Zhuang Liu2 In So Kweon1 Saining Xie3† 1KAIST 2 Meta AI, FAIR 3New York University Code: https://github.com/facebookr...
As a promising scheme of self-supervised learning, masked autoencoding has significantly advanced natural language processing and computer vision. Inspired by this, we propose a neat scheme of masked autoencoders for point cloud self-supervised learning, addressing the challenges posed by point cloud’...
such, our results demonstrate, in various application scenarios, how to perform latent inference and generate samples of data modalities that are unobserved at test time. The generality of this approach allows for applying it to a variety of conditional distributions and variational autoencoder ...
Masked Autoencoding with dBOT [arXiv] [BibTex] This is the official PyTorch implementation ofExploring Target Representations for Masked Autoencoders. News 🎉 January 2024 - The paper is accepted by ICLR 2024. November 2022 - Release the code and pre-trainedmodels. ...
MATE: Masked Autoencoders are Online 3D Test-Time Learners MATE is the first 3D Test-Time Training (TTT) method which makes 3D object recognition architectures robust to distribution shifts which can commonly occur in 3D point clouds. MATE follows the classical TTT paradigm of using an auxiliary...