Skip to content
#

active-learning

Here are 310 public repositories matching this topic...

philippmwirth
philippmwirth commented Sep 24, 2021

Add default parameters for all projection heads

It's helpful to know what the default parameters were in the papers to get started. We should add the default projection head parameters which were used for pre-training on Imagenet to all projection and prediction heads in lightly/models/modules/heads.py.

A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.

  • Updated Feb 10, 2022
  • Python
asreview
chh6936
chh6936 commented Mar 10, 2022

Traceback (most recent call last):
File "tools/train.py", line 257, in
main()
File "tools/train.py", line 170, in main
distributed=distributed, validate=(not args.no_validate), timestamp=timestamp, meta=meta)
File "/media/gc/1T/MI-AOD-master/mmdet/apis/train.py", line 120, in train_detector
runner.run(data_loaders_L, cfg.workflow, cfg.total_epochs)
File "/home/g

SEAL-CI

NOVA is a tool for annotating and analyzing behaviours in social interactions. It supports Annotators using Machine Learning already during the coding process. Further it features both, discrete labels and continuous scores and a visuzalization of streams recorded with the SSI Framework.

  • Updated Mar 16, 2022
  • C#

Improve this page

Add a description, image, and links to the active-learning topic page so that developers can more easily learn about it.

Curate this topic

Add this topic to your repo

To associate your repository with the active-learning topic, visit your repo's landing page and select "manage topics."

Learn more