self-supervised
transferlearning
| self-supervised | transferlearning | |
|---|---|---|
| 2 | 1 | |
| 129 | 13,679 | |
| 3.1% | 0.6% | |
| 0.0 | 7.5 | |
| about 2 years ago | 19 days ago | |
| Python | Python | |
| - | MIT License |
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self-supervised
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[P] solo-learn: a library of self-supervised methods for visual representation learning
W-MSE
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[R] ICLR rejected the submission only for missing large-scale ImageNet experiments
https://github.com/htdt/self-supervised it includes our method, byol and contrastive. I tried to keep the code nice and clean.
transferlearning
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[D] Medium Article: Adaptive Learning for Time Series Forecasting
The src is available in https://github.com/jindongwang/transferlearning I'll also publish about how to code the model for time series
What are some alternatives?
Unsupervised-Classification - SCAN: Learning to Classify Images without Labels, incl. SimCLR. [ECCV 2020]
zshot - Zero and Few shot named entity & relationships recognition
PASS - The PASS dataset: pretrained models and how to get the data
TS-TCC - [IJCAI-21] "Time-Series Representation Learning via Temporal and Contextual Contrasting"
Revisiting-Contrastive-SSL - Revisiting Contrastive Methods for Unsupervised Learning of Visual Representations. [NeurIPS 2021]
awesome-artificial-intelligence-research - A curated list of Artificial Intelligence (AI) Research, tracks the cutting edge trending of AI research, including recommender systems, computer vision, machine learning, etc.
dino - PyTorch code for Vision Transformers training with the Self-Supervised learning method DINO
natural-adv-examples - A Harder ImageNet Test Set (CVPR 2021)
Unsupervised-Semantic-Segmentation - Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals. [ICCV 2021]
FSL-Mate - FSL-Mate: A collection of resources for few-shot learning (FSL).
PaddleHelix - Bio-Computing Platform Featuring Large-Scale Representation Learning and Multi-Task Deep Learning “螺旋桨”生物计算工具集
Transfer-Learning-Library - Transfer Learning Library for Domain Adaptation, Task Adaptation, and Domain Generalization