We summarize various sparse training methods to prune large vision transformers during MAE pretraining and finetun- ing stages, and discuss their shortcomings.
Sparse training is a method of transferring representations from large models to small ones by pruning unimportant parameters. However, naively combining MAE ...
We summarize various sparse training methods to prune large vision transformers during MAE pretraining and finetun- ing stages, and discuss their shortcomings.
SparseMAE: Sparse Training Meets Masked Autoencoders. Aojun Zhou1. Yang Li2 ... SparseMAE-T SparseMAE-S. Input resolution. 512 × 512. Peak learning rate. 1e-4.
SparseMAE: Sparse Training Meets Masked Autoencoders | Request PDF
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Jan 18, 2024 · This paper introduces an end-to-end face recognition network that is invariant to face images with face masks. Conventional face recognition ...
Nov 21, 2022 · In this paper, we develop SMAUG, an efficient pre-training framework for video-language models. The foundation component in SMAUG is masked ...
Oct 15, 2020 · Essentially my data is super sparse and I am attempting to train an autoencoder however my model is learning just to recreate vectors of all ...
Missing: SparseMAE: Meets Masked
The framework uses masked modeling to pre-train the encoder to reconstruct masked human joint trajectories, enabling it to learn generalizable and data ...
We summarize awesome Masked Image Modeling (MIM) and relevent Masked Modeling methods proposed for self-supervised representation learning.
SparseMAE: Sparse Training Meets Masked Autoencoders. A Zhou, Y Li, Z Qin, J Liu, J Pan, R Zhang, R Zhao, P Gao, H Li. Proceedings of the IEEE/CVF International ...