S2-BNN
S2-BNN: Bridging the Gap Between Self-Supervised Real and 1-bit Neural Networks via Guided Distribution Calibration (CVPR 2021) (by szq0214)
MEAL
Official Implementation of MEAL: Multi-Model Ensemble via Adversarial Learning on AAAI 2019 (by AaronHeee)
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.
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.
S2-BNN
Posts with mentions or reviews of S2-BNN.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-12-26.
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[R] Knowledge Distillation, Model Ensemble and Its Application on Visual Recognition - Link to a free online lecture by the author in comments
CVPR 2021 paper ‘S2-BNN: Bridging the Gap Between Self-Supervised Real and 1-bit Neural Networks via Guided Distribution Calibration’, Github: https://github.com/szq0214/S2-BNN
MEAL
Posts with mentions or reviews of MEAL.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-12-26.
What are some alternatives?
When comparing S2-BNN and MEAL you can also consider the following projects:
temporal-shift-module - [ICCV 2019] TSM: Temporal Shift Module for Efficient Video Understanding
MEAL-V2 - MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks. In NeurIPS 2020 workshop.