RHO-Loss VS google-research

Compare RHO-Loss vs google-research and see what are their differences.

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RHO-Loss google-research
1 74
143 27,994
3.5% 3.8%
5.5 9.8
6 months ago 5 days ago
Python Jupyter Notebook
Apache License 2.0 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.


Posts with mentions or reviews of RHO-Loss. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-08-14.


Posts with mentions or reviews of google-research. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-03-16.

What are some alternatives?

When comparing RHO-Loss and google-research you can also consider the following projects:

qdrant - Qdrant - Vector Search Engine and Database for the next generation of AI applications. Also available in the cloud https://cloud.qdrant.io/

Milvus - A cloud-native vector database with high-performance and high scalability.

fast-soft-sort - Fast Differentiable Sorting and Ranking

struct2depth - Models and examples built with TensorFlow

faiss - A library for efficient similarity search and clustering of dense vectors.

ml-agents - The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement learning and imitation learning.

ML-KWS-for-MCU - Keyword spotting on Arm Cortex-M Microcontrollers

rmi - A learned index structure

torchsort - Fast, differentiable sorting and ranking in PyTorch

CLIP - CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image


DALLE-mtf - Open-AI's DALL-E for large scale training in mesh-tensorflow.