HyperGAN VS dnn_from_scratch

Compare HyperGAN vs dnn_from_scratch and see what are their differences.

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HyperGAN dnn_from_scratch
2 1
1,157 21
1.2% -
0.4 0.9
5 months ago 7 months ago
Python Python
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.

HyperGAN

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

dnn_from_scratch

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

What are some alternatives?

When comparing HyperGAN and dnn_from_scratch you can also consider the following projects:

lightweight-gan - Implementation of 'lightweight' GAN, proposed in ICLR 2021, in Pytorch. High resolution image generations that can be trained within a day or two

open-lpr - Open Source and Free License Plate Recognition Software

student-teacher-anomaly-detection - Student–Teacher Anomaly Detection with Discriminative Latent Embeddings

DETReg - Official implementation of the paper "DETReg: Unsupervised Pretraining with Region Priors for Object Detection".

deepxde - A library for scientific machine learning

Mask-RCNN-TF2 - Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow 2.0

guesslang - Detect the programming language of a source code

ML-Optimizers-JAX - Toy implementations of some popular ML optimizers using Python/JAX