dnn_from_scratch VS HyperGAN

Compare dnn_from_scratch vs HyperGAN and see what are their differences.

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

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.

HyperGAN

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

What are some alternatives?

When comparing dnn_from_scratch and HyperGAN 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

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

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

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

deepxde - A library for scientific machine learning

guesslang - Detect the programming language of a source code

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

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