Efficient Training of Visual Transformers with Small Datasets

Last update: Dec 25, 2022

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Overview

Efficient Training of Visual Transformers with Small Datasets

To appear in NerIPS 2021.

[arXiv][code]
Yahui Liu^1,3, Enver Sangineto¹, Wei Bi², Nicu Sebe¹, Bruno Lepri³, Marco De Nadai³
¹University of Trento, Italy, ²Tencent AI Lab, China, ³Bruno Kessler Foundation, Italy.

Coming soon ...

Owner

Yahui Liu

Yahui Liu currently studies in Multimedia and Human Understanding Group (MHUG), University of Trento, Italy.

GitHub Repository

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Efficient Training of Visual Transformers with Small Datasets

Related tags

Overview

Efficient Training of Visual Transformers with Small Datasets

Owner

Yahui Liu

This repository contains the segmentation user interface from the OpenSurfaces project, extracted as a lightweight tool

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Efficient Training of Visual Transformers with Small Datasets

Related tags

Overview

Efficient Training of Visual Transformers with Small Datasets

Owner

Yahui Liu

This repository contains the segmentation user interface from the OpenSurfaces project, extracted as a lightweight tool

Behind the Curtain: Learning Occluded Shapes for 3D Object Detection

FAMIE is a comprehensive and efficient active learning (AL) toolkit for multilingual information extraction (IE)

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NEATEST: Evolving Neural Networks Through Augmenting Topologies with Evolution Strategy Training

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Vpw analyzer - A visual J1850 VPW analyzer written in Python

Mercer Gaussian Process (MGP) and Fourier Gaussian Process (FGP) Regression

DTCN SMP Challenge - Sequential prediction learning framework and algorithm

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Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning, NeurIPS 2021 (Spotlight)

Code for "FPS-Net: A convolutional fusion network for large-scale LiDAR point cloud segmentation".

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The code for paper "Contrastive Spatio-Temporal Pretext Learning for Self-supervised Video Representation" which is accepted by AAAI 2022

List of content farm sites like g.penzai.com.

This is the official implementation for "Do Transformers Really Perform Bad for Graph Representation?".

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​ This is the Pytorch implementation of Progressive Attentional Manifold Alignment.

Code for You Only Cut Once: Boosting Data Augmentation with a Single Cut

This is the Pytorch implementation of Progressive Attentional Manifold Alignment.