self-supervised VS Unsupervised-Classification

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

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self-supervised Unsupervised-Classification
2 2
129 1,393
3.1% 1.9%
0.0 1.4
about 2 years ago over 1 year 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.

Unsupervised-Classification

Posts with mentions or reviews of Unsupervised-Classification. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-04-13.

What are some alternatives?

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

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

simclr - SimCLRv2 - Big Self-Supervised Models are Strong Semi-Supervised Learners

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

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

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

PaddleHelix - Bio-Computing Platform Featuring Large-Scale Representation Learning and Multi-Task Deep Learning “螺旋桨”生物计算工具集

SimMIM - This is an official implementation for "SimMIM: A Simple Framework for Masked Image Modeling".

contrastive-reconstruction - Tensorflow-keras implementation for Contrastive Reconstruction (ConRec) : a self-supervised learning algorithm that obtains image representations by jointly optimizing a contrastive and a self-reconstruction loss.

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

DiffCSE - Code for the NAACL 2022 long paper "DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings"

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Bad PDFs = bad UX. Slow load times, broken annotations, clunky UX frustrates users. Nutrient’s PDF SDKs gives seamless document experiences, fast rendering, annotations, real-time collaboration, 100+ features. Used by 10K+ devs, serving ~half a billion users worldwide. Explore the SDK for free.
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