All Questions
Tagged with anomaly-detectiondeep-learning
30 questions
2votes
0answers
28views
PyGOD memory error despite batch size argument
Anyone know why PyGOD's DOMINANT implementation produces a memory error even though the batch size argument is reasonable? To reproduce: ...
- 121
2votes
1answer
135views
balancing and imbalancing in supervised anomaly detection probelm
I am dealing with a supervised anomaly detection problem, where I have labels with 0 for normal and 1 for abnormal. The default distribution of the dataset is highly imbalanced with a ratio of 96:4 ...
- 21
0votes
1answer
188views
Anomaly Detection in Log Data using LSTM
Problem Overview: I am currently working on a project involving anomaly detection in log data. The anomalies are defined by deviations from historical patterns. The log data has a simple structure: [...
- 11
1vote
1answer
202views
How to Justify Anomalies Detected by Unsupervised Anomaly Detection Models? [closed]
I'm working on an unsupervised anomaly detection project involving a large sensor dataset, where I aim to identify anomalies without the aid of labeled data. While I've implemented several ...
0votes
1answer
119views
Use computer vision to detect door blockage
I want to detect door blockage on a camera. Basically if the exit door is blocked by an object, it detects it as an anomaly. How can we do it? Is it possible to do it using OpenCV? Remember, it doesn’...
- 237
0votes
1answer
44views
Best practice labeling grouped anomalies for object detection
I would like to train object detection model (e.g. YOLO) for images that contain anomalies. The anomalies are essentially the holes in a surface of different sizes. How do I label correctly such ...
1vote
1answer
22views
Underfitting and perfomance metrics in unsupervised methods
My question is simple and yet quite hard to find an answer to. In an unsupervised method, for example, when you have to reconstruct an input, how can you tell if your loss is good enough? Generally, ...
-2votes
1answer
196views
How to increase , precision-recall value in your Deep learning model
I am getting good accuracy metrics around 80 with precision =66, recall =37, F1 =47. How can I improve precision, and recall metrics in anomaly detection scenarios.. any suggestions?
- 171
0votes
1answer
148views
What are some state of art computer vision models for anomaly detection that can learn continuously and build classes for detected anomalies?
I'm looking forward to build a model that: Detect anomalies Improve over user feedback Build classes for the anomalies based on user feedback Since a schema is worth a thousand words: Do you know ...
- 111
1vote
1answer
33views
Train classifier to detect crest of wave
In the picture, there are data and a target in the time series. The data is padded until it reaches the max length. The target is marked by humans. It pins down the starting point and stopping point ...
- 111
0votes
0answers
450views
How can realize the evaluation/validation of unsupervised models through unlabeled data?
I'm researching anomaly detection, which is nothing else than outliers detection on a set of time-series web servers access log data or network traffic. Recently I re-faced to following fundamental ...
- 527
3votes
1answer
82views
What's the best way to validate a rare event detection model during training?
When training a deep model for rare event detection (e.g. sound of an alarm in a home device audio stream), is it best to use a balanced validation set (50% alarm, 50% normal) to determine early ...
- 71
2votes
1answer
194views
Machine Learning Techinques that Automate Fast Fourier Transform
I have a 40k Hz time-series data of vibration, which is used to predict equipment failure. The goal here is to make a system that predicts it automatically. I am thinking of a couple of ways but not ...
- 21
1vote
1answer
60views
Autoencoder anamoly detection
I recently learnt about the anamoly detection using autoencoders(specifically denoisinng autoencoders).To train the autoencoders do we need a data having some pattern? or is there some way to ...
1vote
0answers
33views
How to handle data with dependency on two different dates
I am currently dealing with a dataset that contains multiple date-time fields: "buy-date" and "receive-date" which both have an effect on the prices and amount of offers made. One example could be: <...
- 13
