feat: add GaussianMLPVAE model: A simple implementation of Gaussian MLP Encoder and Decoder add VQ-VAE model: https://ar5iv.labs.arxiv.org/html/1711.00937 | https://avdnoord.github.io/homepage/vqv...
"model_id": "wfvae", "from_pretrained": null, "base_channels": 128, "decoder_energy_flow_hidden_size": 128, "decoder_num_resblocks": 2, "dropout": 0.0, "encoder_energy_flow_hidden_size": 128, "encoder_num_resblocks": 2, "latent_dim": 4, "use_attention": tr...
This study first attempts to explore different mathematical properties of the VAE model, in particular, the theoretical framework of the encoding and decoding processes, the possible achievable lower bound and loss functions of different applications; then applies the established VAE model to generate ...
search filter_listFilters AllYour WorkShared With YouBookmarks Hotness vae_celebaNotebook copied with edits from Flo Leberfinger· Updated 3y ago 0 comments· celeba_vae_models +1 arrow_drop_up0more_horiz vae_testUpdated 3y ago 1 comment· celeba_vae +2 arrow_drop_up0more_horiz...
图像生成,是多模态的一个重要分支,事实上在LLM出现以前,图像生成的技术栈就已经发展了很久,如原始的Auto Encoder,以及躁动一时的GAN,还有大名鼎鼎的Difussion Model,这篇文章会大致介绍一下图像生成的原理,然后逐次介绍VAE、GAN和Diffusion Model的核心知识点发布...
In recent decades, the Variational AutoEncoder (VAE) model has shown good potential and capability in image generation and dimensionality reduction. The combination of VAE and various machine learning frameworks has also worked effectively in different daily life applications, however its possible use an...
深入细致剖析了DPM模型原理,以及与VAE模型、Mixture模型之间的联系。 理论上分析出"数据分布 q(x) 向噪声分布 q(zT) 转换的规律"。 理论上证明了"优化重建项( ∬q(x)q(z1|x)logp(x|z1)dxdz1 )的本质是使 p(x|z1)去拟合 q(x|z1)"。 理论上证明了"优化一致项( ∬q(zt,x)KL[q(...
This repo contains the codes for our paper: Molecule Generation by Principal Subgraph Mining and Assembling. - PS-VAE/src/pl_models/ps_vae_model.py at 0dfcbd25b91e1127e556cb3c273260a41a1c3f35 · THUNLP-MT/PS-VAE
标题(学术版):Diffusion Model与VAE在生成模型中的地位探讨 标题(生动版):探索Diffusion Model与VAE的奇妙世界 摘要:Diffusion Model和Variational Autoencoder(VAE)是两种重要的生成模型,它们在数据生…
VAE模型则是学会了不同维度的一个分布情况,均值和方差,如果各个分布你都采样到最大的那个值,代表的是一个大众脸,人脸可能不是符合正太分布的,但是其眉毛的宽度、长度、粗细、鼻子的高度、宽度、肤色等等可能是符合随机分布的,训练时我们倾向让每个分布都符合标准整体分布,推理是对这些分布再进行采样,模型具备在平均脸...