Supervised Anomaly Detection Model Collection
Machine Learning and Deep Learning models to detect anomalies using time series data.
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Machine Learning and Deep Learning models to detect anomalies using time series data.
Model able to generate a production plan for a week given a set of orders that minimizes the monetary loss .
It is a service for defect detection and defect localization in hard metal industry
EurekAD is a service for anomaly detection in Industrial time series data.
An open-source LLaMa2 language model of 7b parameters fine-tuned (using as base model NousResearch/Nous-Hermes-llama-2-7b) to follow instructions in italian.
This model analyses the input text and provides an answer whether in the text there is a change of topic or not (resp. TOPPICCHANGE, SAMETOPIC).
A masked contrastive learning framework for learning meaningful fine-grained representations with coarse-labeled dataset.
A self-supervised learning method aiming to alleviate the inherent false-negative problem in contrastive learning framework.
A robust and efficient training framework tackling with dataset with noisy labels.
A self-supervised pre-training method with focus on alleviating class collision problem using a cross-context learning scheme.