Lavreniuk / Pseudo-labelling-and-knowledge-distillation-from-multiple-teachersLinks
Pseudo-labelling and knowledge distillation from multiple teachers for remote sensing monitoring of deforestation in Ukraine
☆13Updated 2 years ago
Alternatives and similar repositories for Pseudo-labelling-and-knowledge-distillation-from-multiple-teachers
Users that are interested in Pseudo-labelling-and-knowledge-distillation-from-multiple-teachers are comparing it to the libraries listed below
Sorting:
- It contains some of the novel feature selection algorithms I've developed☆14Updated 4 years ago
- ☆17Updated 4 years ago
- Pytorch implementation of four neural network based domain adaptation techniques: DeepCORAL, DDC, CDAN and CDAN+E. Evaluated on benchmark…☆91Updated 3 years ago
- Python (PyTorch) realization of Deep Feature Selection (Model, Algorithm)☆17Updated 5 years ago
- [TAI 2023] Contrastive Domain Adaptation for Time-Series via Temporal Mixup☆31Updated last year
- Post-selection inference based on truncated Gaussians for the HSIC-Lasso feature selection procedure☆10Updated 4 years ago
- An Explainable Neural Network for Fault Diagnosis With a Frequency Activation Map☆14Updated 3 years ago
- Pseudo Labeling for Neural Networks and Logistic Regression/SVMs ( Based on "Pseudo-Label : The Simple and Efficient Semi-Supervised Lear…☆73Updated 5 years ago
- A Semi-supervised learning model (Ladder Network) to classify MNIST digits. A few attacks were executed on it with the target of misclass…☆10Updated 2 years ago
- Feature selection for deep learning models.☆12Updated 4 years ago
- DyEdgeGAT: Dynamic Edge via Graph Attention for Early Fault Detection in IIoT Systems☆16Updated 2 weeks ago
- Pytorch implementation of "DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data".☆120Updated 4 years ago
- FeatTS is a Semi-Supervised Clustering method that leverages features extracted from the raw time series to create clusters that reflect …☆17Updated last year
- Contrastive Adversarial Learning for Multi-Source Time Series Domain Adaptation☆28Updated 3 years ago
- Code and data for our paper on IEEE-TIE: Integrating Expert Knowledge with Domain Adaptation for Unsupervised Fault Diagnosis☆41Updated 2 years ago
- Comparing a transormer GAN and a LSTM GAN for augmenting timeseries datasets☆14Updated last year
- [TFS 2020] Deep Fuzzy K-Means with Adaptive Loss and Entropy Regularization☆34Updated 3 years ago
- Code for our paper Self Supervised Learning for Semi Supervised Time Series Classification PAKDD 2020☆16Updated 4 years ago
- A deep transfer regression method based on seed replacement considering balanced domain adaptation☆15Updated 2 years ago
- MHCCL: Masked Hierarchical Cluster-wise Contrastive Learning for Multivariate Time Series - a PyTorch Version (AAAI-2023)☆41Updated last year
- Codebase for "SGAIN, WSGAIN-CP and WSGAIN-GP: Novel GAN Methods for Missing Data Imputation"☆16Updated 3 years ago
- The code of Interpretable Convolutional Neural Network with Multilayer Wavelet for Noise-Robust Machinery Fault Diagnosis☆44Updated 3 years ago
- SSIM - A Deep Learning Approach for Recovering Missing Time Series Sensor Data☆39Updated 4 years ago
- Contrastive Learning for Domain Adaptation of Time Series☆91Updated last year
- Record my work in transfer learning during my postgraduate period.☆50Updated last year
- Multilevel Wavelet Decomposition Network for Interpretable Time Series Analysis☆33Updated 5 years ago
- Multi-source Heterogeneous Domain Adaptation with Conditional Weighting Adversarial Network, TNNLS 2021☆20Updated 3 years ago
- ☆25Updated 9 months ago
- The PyTorch version for 'Domain-adversarial similarity-based meta-learning network'.☆16Updated 3 years ago
- Translation of DeepJDOT into pytorch.☆10Updated 4 years ago