transferlearning VS train-procgen

Compare transferlearning vs train-procgen and see what are their differences.

train-procgen

Code for the paper "Leveraging Procedural Generation to Benchmark Reinforcement Learning" (by openai)
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transferlearning train-procgen
1 1
12,867 166
- 0.0%
7.8 0.0
9 days ago about 1 year ago
Python Python
MIT License MIT License
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.

transferlearning

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

train-procgen

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

What are some alternatives?

When comparing transferlearning and train-procgen you can also consider the following projects:

zshot - Zero and Few shot named entity & relationships recognition

Tacotron-2 - DeepMind's Tacotron-2 Tensorflow implementation

stackoverflow-better-stats - Better statistics about Stack Overflow's 2023 Developer Survey

Paperless - Scan, index, and archive all of your paper documents

PaddleHelix - Bio-Computing Platform Featuring Large-Scale Representation Learning and Multi-Task Deep Learning “螺旋桨”生物计算工具集

gpt-2 - Code for the paper "Language Models are Unsupervised Multitask Learners"

awesome-artificial-intelligence-research - A curated list of Artificial Intelligence (AI) Research, tracks the cutting edge trending of AI research, including recommender systems, computer vision, machine learning, etc.

qlib - Qlib is an AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas to implementing productions. Qlib supports diverse machine learning modeling paradigms. including supervised learning, market dynamics modeling, and RL.

TS-TCC - [IJCAI-21] "Time-Series Representation Learning via Temporal and Contextual Contrasting"

ALAE - [CVPR2020] Adversarial Latent Autoencoders

Transfer-Learning-Library - Transfer Learning Library for Domain Adaptation, Task Adaptation, and Domain Generalization

Efficient-VDVAE - Official Pytorch and JAX implementation of "Efficient-VDVAE: Less is more"