self-supervised VS PaddleHelix

Compare self-supervised vs PaddleHelix and see what are their differences.

self-supervised

Whitening for Self-Supervised Representation Learning | Official repository (by htdt)
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self-supervised PaddleHelix
2 1
129 1,039
3.1% 1.6%
0.0 8.3
about 2 years ago 3 months ago
Python Python
- GNU General Public License v3.0 or later
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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self-supervised

Posts with mentions or reviews of self-supervised. We have used some of these posts to build our list of alternatives and similar projects.

PaddleHelix

Posts with mentions or reviews of PaddleHelix. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing self-supervised and PaddleHelix you can also consider the following projects:

Unsupervised-Classification - SCAN: Learning to Classify Images without Labels, incl. SimCLR. [ECCV 2020]

dipy - DIPY is the paragon 3D/4D+ medical imaging library in Python. Contains generic methods for spatial normalization, signal processing, machine learning, statistical analysis and visualization of medical images. Additionally, it contains specialized methods for computational anatomy including diffusion, perfusion and structural imaging.

PASS - The PASS dataset: pretrained models and how to get the data

DeBERTa - The implementation of DeBERTa

Revisiting-Contrastive-SSL - Revisiting Contrastive Methods for Unsupervised Learning of Visual Representations. [NeurIPS 2021]

eirli - An Empirical Investigation of Representation Learning for Imitation (EIRLI), NeurIPS'21

dino - PyTorch code for Vision Transformers training with the Self-Supervised learning method DINO

Unsupervised-Semantic-Segmentation - Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals. [ICCV 2021]

typedb-ml - TypeDB-ML is the Machine Learning integrations library for TypeDB

transferlearning - Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习

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Other PDF SDKs promise a lot - then break. Laggy scrolling, poor mobile UX, tons of bugs, and lack of support cost you endless frustrations. Nutrient’s SDK handles billion-page workloads - so you don’t have to debug PDFs. Used by ~1 billion end users in more than 150 different countries.
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