358 Open source projects that are alternatives of or similar to deviation-network

A scikit-learn compatible library for anomaly detection

Code for reproducing f-AnoGAN in Pytorch

Supplementary material for IJCNN paper "XGBOD: Improving Supervised Outlier Detection with Unsupervised Representation Learning"

A PyTorch implementation of Deep SAD, a deep Semi-supervised Anomaly Detection method.

A Python Toolbox for Scalable Outlier Detection (Anomaly Detection)

Supplementary material for KDD 2018 workshop "DCSO: Dynamic Combination of Detector Scores for Outlier Ensembles"

Anomaly detection related books, papers, videos, and toolboxes

Sinkhorn Label Allocation is a label assignment method for semi-supervised self-training algorithms. The SLA algorithm is described in full in this ICML 2021 paper: https://arxiv.org/abs/2102.08622.

GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training

(Python, R, C++) Explainable outlier/anomaly detection through decision tree conditioning

Official PyTorch implementation of the paper “Explainable Deep Few-shot Anomaly Detection with Deviation Networks”, weakly/partially supervised anomaly detection, few-shot anomaly detection, image defect detection.

TOD: GPU-accelerated Outlier Detection via Tensor Operations

Main component extraction for outlier detection

Enhancing the BERT training with Semi-supervised Generative Adversarial Networks

A curated list of papers, code and resources pertaining to weak-shot classification, detection, and segmentation.

ADRepository: Real-world anomaly detection datasets

List of tools & datasets for anomaly detection on time-series data.

A Deep Graph-based Toolbox for Fraud Detection in TensorFlow 2.X

Implementation of TheWebConf 2021 -- Few-shot Network Anomaly Detection via Cross-network Meta-learning

PyProphet: Semi-supervised learning and scoring of OpenSWATH results.

Tensorflow implementation for paper: Learning to Compare: Relation Network for Few-Shot Learning.

A Julia package for robust regressions using M-estimators and quantile regressions

Tensorflow Implementation on Paper [ECCV2018]Semi-Supervised Deep Learning with Memory

Universal Weakly Supervised Segmentation by Pixel-to-Segment Contrastive Learning

ICML'2022: Black-Box Tuning for Language-Model-as-a-Service

EC-GAN: Low-Sample Classification using Semi-Supervised Algorithms and GANs (AAAI 2021)

a deep recurrent model for exchangeable data

MemStream: Memory-Based Streaming Anomaly Detection

Repository for detecting outliers using Grubb's Threshold & Generalized Extreme Studentized Deviate (ESD) Test

Matching Networks for one-shot learning in tensorflow (NIPS'16)

Experiments on Flood Segmentation on Sentinel-1 SAR Imagery with Cyclical Pseudo Labeling and Noisy Student Training

Mean-Shifted Contrastive Loss for Anomaly Detection

Weakly Supervised End-to-End Learning (NeurIPS 2021)

Semi-supervised Learning GAN

DDC-Outlier: Preventing medication errors using unsupervised learning

An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.

[AAAI 2019] Weakly-Supervised Hierarchical Text Classification

PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation (CVPR 2021)

JCLAL is a general purpose framework developed in Java for Active Learning.

Official source code of "Fast Adaptive RNN Encoder-Decoder for Anomaly Detection in SMD Assembly Machine"

Weakly Supervised Dense Event Captioning in Videos, i.e. generating multiple sentence descriptions for a video in a weakly-supervised manner.

Official pytorch implementation of the paper: "A Hierarchical Transformation-Discriminating Generative Model for Few Shot Anomaly Detection"

https://events.kaspersky.com/hackathon/

Semisupervided LDA for theory-driven text analysis

anomaly detection with anomalize and Google Trends data

Source code and data for ACL 2019 Long Paper ``Semantic Parsing with Dual Learning".

Joint Semi-supervised Learning of Image Registration and Segmentation

The Combined Anomalous Object Segmentation (CAOS) Benchmark

Pytorch implementations of generative models: VQVAE2, AIR, DRAW, InfoGAN, DCGAN, SSVAE

A non-official 100% PyTorch implementation of META-DATASET benchmark for few-shot classification

A parallel implementation of local outlier factor based on Spark

TensorFlow implementation for "Guided Optical Flow Learning"

Example of Anomaly Detection using Convolutional Variational Auto-Encoder (CVAE)

[ICCV'21] Official PyTorch implementation of Relational Embedding for Few-Shot Classification

Benchmarks for semi-supervised domain generalization.

A quick and dirty vehicle speed detector using video + anomaly detection

[ti]ny [li]ttle machine learning [tool]box - Machine learning, anomaly detection, one-class classification, and structured output prediction

This project is a semi-supervised approach to detect emotions on faces in-the-wild using GAN

PyTorch Implementation of ByteDance's Cross-speaker Emotion Transfer Based on Speaker Condition Layer Normalization and Semi-Supervised Training in Text-To-Speech

A curated list of papers, code and resources pertaining to few-shot image generation.