Handle class imbalance intelligently by using variational auto-encoders to generate synthetic observations of your minority class.
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Updated
Apr 6, 2017 - Python
Handle class imbalance intelligently by using variational auto-encoders to generate synthetic observations of your minority class.
Text classification with scikit-learn, used to make predictions for Kaggle Spooky Author Identification competition
Theano implementation of Cost-Sensitive Deep Neural Networks
labs for PRML
P. Domingos proposed a principled method for making an arbitrary classifier cost-sensitive by wrapping a cost-minimizing procedure around it. The procedure, called MetaCost, treats the underlying classifier as a black box, requiring no knowledge of its functioning or change to it.
The PyTorch implementation of Discriminant Distribution-Agnostic loss (DDA loss)
Supplementary codes of the Master Thesis "Binary Classification on Imbalanced Datasets"
This is the repository for implementation of 'Knowledge Distillation for Multi-task Learning'
Electricity Fraud Detection in Smart Grids
Cost-Sensitive Learning / ReSampling / Weighting / Thresholding / BorderlineSMOTE / AdaCost / etc.
Experimental analysis of KNN by using waveform dataset
[NeurIPS 2020] Semi-Supervision (Unlabeled Data) & Self-Supervision Improve Class-Imbalanced / Long-Tailed Learning
Software implementation of the manuscript "A Two-step Anomaly detection Based Method for PU Classification in Imbalanced Data Sets".
Fake News detection based on the FA-KES dataset
Implement GANs to generate time-series signals for imbalanced learning problem. The experiments are conducted using CWRU bearing data.
[NeurIPS 2020] Balanced Meta-Softmax for Long-Tailed Visual Recognition
🎲 Iterable dataset resampling in PyTorch
[CVPR 2022] Rethinking Depth Estimation for Multi-View Stereo: A Unified Representation
[ICML 2021, Long Talk] Delving into Deep Imbalanced Regression
PU Hellinger Trees is a technique for positive and unlabeled imbalanced data.
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