CycleGAN VS anycost-gan

Compare CycleGAN vs anycost-gan and see what are their differences.

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CycleGAN anycost-gan
2 1
12,138 769
- 0.3%
2.5 2.5
8 months ago 7 months ago
Lua Python
GNU General Public License v3.0 or later MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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CycleGAN

Posts with mentions or reviews of CycleGAN. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-04-22.

anycost-gan

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

What are some alternatives?

When comparing CycleGAN and anycost-gan you can also consider the following projects:

pix2pix - Image-to-image translation with conditional adversarial nets

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

Deep-Exemplar-based-Video-Colorization - The source code of CVPR 2019 paper "Deep Exemplar-based Video Colorization".

pytorch-CycleGAN-and-pix2pix - Image-to-Image Translation in PyTorch

pix2pixHD - Synthesizing and manipulating 2048x1024 images with conditional GANs

PaddleGAN - PaddlePaddle GAN library, including lots of interesting applications like First-Order motion transfer, Wav2Lip, picture repair, image editing, photo2cartoon, image style transfer, GPEN, and so on.

Anime-face-generation-DCGAN-webapp - A port of my Anime face generation using Pytorch into a Webapp

contrastive-unpaired-translation - Contrastive unpaired image-to-image translation, faster and lighter training than cyclegan (ECCV 2020, in PyTorch)

stylegan-waifu-generator - Generate your waifu with styleGAN, stylegan老婆生成器

faceswap-GAN - A denoising autoencoder + adversarial losses and attention mechanisms for face swapping.

DeepSIM - Official PyTorch implementation of the paper: "DeepSIM: Image Shape Manipulation from a Single Augmented Training Sample" (ICCV 2021 Oral)