Auto^6ML is a open-source library for machine learning automation. It is based entirely on jittor, offering high performance and faster speeds. The package supports algorithms based on SLeM(Simulating Learning Methodology ) and some popular meta-learning algorithms.
Our library is divided by methods, which include:
- Data Automation methods
- Network Automation methods
- Loss Automation methods
- Algorithm Automation methods
The currently supported algorithms include:
Data Automation methods[Code]
- L2W- Learning to Reweight Examples for Robust Deep Learning [ICML 2018] [Code]
Data Automation methods[Code]
- MWNet- Learning an Explicit Mapping For Sample Weighting [NeurIPS 2019] [Code]
- PMWNet- A Probabilistic Formulation for Meta-Weight-Net [TNNLS 2021] [Code]
- CMWNet- CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep Learning [TPAMI 2023] [Code]
- DAC-MR-WNet- sample weighting for robust deep learning [submitted] [Code]
- OS3M- Learning a Trustworthy Sample Selection Strategy for Open-Set Weakly-Supervised Learning [[submitted]] [Code]
- MSLC- label corrector for noisy labels learning [AAAI 2021] [Code]
- CMWNet-SL- an adaptive robust algorithm for sample selection and label correction [NSR 2023] [Code]
Network Automation[Code]
- DPIR- Plug-and-Play Image Restoration With Deep Denoiser Prior [TPAMI 2022] [Code]
- L2AC- Imbalanced Semi-supervised Learning with Bias Adaptive Classifier [ICLR 2023] [Code]
- MTA- Meta Transition Adaptation for Robust Deep Learning with Noisy Labels [submitted] [Code]
Loss Automation[Code]
- HWNet4ACHN- Learning an Explicit Weighting Scheme for Adapting Complex HSI Noise [CVPR 2021] [Code]
- HWNet4HID- A Hyper-weight Network for Hyperspectral Image Denoising [submitted] [Code]
- NARL- Improve noise tolerance of robust loss via noise-awareness [TNNLS 2024] [Code]
Algorithm Automation[Code]
- MLR- Improve noise tolerance of robust loss via noise-awareness [TPAMI 2023] [Code]
- RG- Understanding the Generalization of Bilevel Programming in Hyperparameter Optimization: A Tale of Bias-Variance Decomposition [[submitted]] [Code]
[1] Jun Shu, Zongben Xu, Deyu Meng. Small sample learning in big data era. 2018.
[1] Jun Shu, Deyu Meng, Zongben Xu. Learning an explicit hyperparameter prediction policy conditioned on tasks. JMLR, 2023.