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simple-cnaps

Source codes for "Improved Few-Shot Visual Classification" (CVPR 2020), "Enhancing Few-Shot Image Classification with Unlabelled Examples" (WACV 2022) and "Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning" (TPAMI 2022 - in submission)

simple_shot

simtrans-weak-shot-classification

[NeurIPS 2021] The first weak-shot classification paper.

sincnet

SincNet is a neural architecture for efficiently processing raw audio samples.

sklearn-doc-zh

:book: [译] scikit-learn（sklearn） 中文文档

slk-few-shot

Clustering for Few-shot Learning

slmr

The implement of An Unsupervised Short- and Long-Term Mask Representation for Multivariate Time Series Anomaly Detection

smartfew

SmartFew is your swiss knife for semi-supervised structuring of unlabeled data using Few Shot Learning.

soh-estimation

Lithium ion battery state of health estimation and remaining useful life prediction using ELM

sound-recognition-tutorial

A simple sound recognition tutorial, including data analysis, feature extraction, model building, model train and model test ...

splicing-learning

A novel learning method for few-shot leanring

ssformers

talos

Hyperparameter Optimization for TensorFlow, Keras and PyTorch

team-learning

主要展示Datawhale的组队学习计划。

telegramgroup

悄咪咪收集的1000+个Telegram群合集，如果有更多好玩的telegram群，欢迎在 issue 提出或者pull requests

telemanom-dataset

A framework for using LSTMs to detect anomalies in multivariate time series data. Includes spacecraft anomaly data and experiments from the Mars Science Laboratory and SMAP missions.

time-series-anomaly

Anomaly detection on time series using Deep Learning techniques

time-series-anomaly-detection

Repository for the paper titled "Attention-based Bi-LSTM for Anomaly Detection on Time-Series Data"

time-series-classification-for-human-activity-recognition

An implementation of the CNN-LSTM model for Classifying Time Series Human Activities

time-series-transformers-review

A professionally curated list of awesome resources (paper, code, data, etc.) on transformers in time series.

timehetnet

tips-of-feature-engineering

A feature engineering kit for each issue, to give you a deeper and deeper understanding of the work of feature engineering!

tisat

tl_rul

Application of Transfer Learning for RUL Prediction

tlidb

toyadmos-dataset

Dataset and its sample codes for anomaly detection in sound.

tpn

Code for ICLR19 paper: Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning.

training-noise-robust-deep-neural-networks-via-meta-learning

Code for CVPR2020 ‘Training Noise Robust Deep Neural Networks via Meta-Learning’

tramas-weak-shot-object-detection

[NeurIPS 2021] A weak-shot object detection approach by transferring semantic similarity and mask prior.

tranad

[VLDB'22] Anomaly Detection using Transformers, self-conditioning and adversarial training.