awesome-metric-learning
awesome-TS-anomaly-detection
| awesome-metric-learning | awesome-TS-anomaly-detection | |
|---|---|---|
| 3 | 72 | |
| 433 | 2,822 | |
| 0.5% | - | |
| 1.8 | 0.0 | |
| about 1 year ago | 3 months ago | |
| Creative Commons Zero v1.0 Universal | - |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
awesome-metric-learning
-
Create Your Own Custom Plugins for ChatGPT 🎉 Browse the Web, Execute Code, Use APIs 🛠️
And, for resources on similarity learning at large, you may want to check out this annotated list: https://github.com/qdrant/awesome-metric-learning
-
Similarity Learning lacks a framework. So we built one
Some loss functions such as ArcFace loss and CosFace loss enforce the encoder model to organize their latent space in such a way that categories are placed with an angular margin from one another. Thus the model implicitly learns a continuous distance function.
Fun fact, one of the examples in Quaterion is for similar cars search.
If you find this topic and want to discover more, we collected a bunch of resources that might be helpful. https://github.com/qdrant/awesome-metric-learning
-
Awesome Metric Learning!
The Metric Learning approach to data science problems is heavily underutilized. There are a lot of academic papers around it but much fewer practical guides and tutorials. So we decided that we could help people adopt metric learning by collecting related materials in one place. We are publishing a curated list of awesome practical metric learning tools, libraries, and materials - https://github.com/qdrant/awesome-metric-learning This collection aims to put together references to all required materials for building your application using Metric Learning. It is open-source, PR's are more than welcome!
awesome-TS-anomaly-detection
What are some alternatives?
build-your-own-x - Master programming by recreating your favorite technologies from scratch.
Awesome-Geospatial - Long list of geospatial tools and resources
finetuner - :dart: Task-oriented embedding tuning for BERT, CLIP, etc.
openHistorian - The Open Source Time-Series Data Historian
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
Netdata - The open-source observability platform everyone needs
contract-discovery - Data and additional information regarding the paper: Contract Discovery. Dataset and a Few-Shot Semantic Retrieval Challenge with Competitive Baselines (to appear in Findings of EMNLP).
NAB - The Numenta Anomaly Benchmark
quaterion - Blazing fast framework for fine-tuning similarity learning models
A3 - Inspired by recent advances in coverage-guided analysis of neural networks, we propose a novel anomaly detection method. We show that the hidden activation values contain information useful to distinguish between normal and anomalous samples. Our approach combines three neural networks in a purely data-driven end-to-end model. Based on the activation values in the target network, the alarm network decides if the given sample is normal. Thanks to the anomaly network, our method even works in strict semi-supervised settings. Strong anomaly detection results are achieved on common data sets surpassing current baseline methods. Our semi-supervised anomaly detection method allows to inspect large amounts of data for anomalies across various applications.
Milvus - A cloud-native vector database, storage for next generation AI applications
pyod - A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)