SUBLIME VS image-crop-analysis

Compare SUBLIME vs image-crop-analysis and see what are their differences.

SUBLIME

Semantically Understanding Bias with LIME. Using the LIME-RS Algorithm to understand bias in recommender systems. (by FurkanToprak)

image-crop-analysis

Code for reproducing our analysis in the paper titled: Image Cropping on Twitter: Fairness Metrics, their Limitations, and the Importance of Representation, Design, and Agency (by twitter-research)
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SUBLIME image-crop-analysis
1 2
3 249
- 0.8%
10.0 0.0
over 2 years ago over 2 years ago
Jupyter Notebook Jupyter Notebook
- Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.

SUBLIME

Posts with mentions or reviews of SUBLIME. We have used some of these posts to build our list of alternatives and similar projects.

image-crop-analysis

Posts with mentions or reviews of image-crop-analysis. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing SUBLIME and image-crop-analysis you can also consider the following projects:

qt-dab - Qt-DAB, a general software DAB (DAB+) decoder with a (slight) focus on showing the signal

vqgan-clip-generator - Implements VQGAN+CLIP for image and video generation, and style transfers, based on text and image prompts. Emphasis on ease-of-use, documentation, and smooth video creation.

engineering-management - A collection of inspiring resources related to engineering management and tech leadership

Face-Mask-Detection - Face Mask Detection system based on computer vision and deep learning using OpenCV and Tensorflow/Keras

interpret - Fit interpretable models. Explain blackbox machine learning.

oemer - End-to-end Optical Music Recognition (OMR) system. Transcribe phone-taken music sheet image into MusicXML, which can be edited and converted to MIDI.

shapash - 🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

neural-style-transfer - :paintbrush: This repository contains, well-structured Python library and runnable fully prepared Python notebook of the "Neural Style Transfer" algorithm