nvae a deep hierarchical variational autoencoder github

NVAEs design focuses on tackling two main challenges: (i) designing expressive neural networks specifically for VAEs, and (ii) scaling up the training to a large number of hierarchical groups and image sizes while maintaining training stability. For all the datasets but FFHQ, we follow Glow[kingma2018glow] for the train and test splits. In contrast, NVAE is trained directly with the VAE objective. We believe the root cause smaller. In the batch normalization layers during sampling, we examine two settings: i) the default mode that uses the running averages from training (on the left), and ii) readjusted mode in which the running averages are re-tuned by sampling from the model 500 times with the given temperature (on the right). details. For FFHQ experiments, we reduce the learning rate to 0.008 to further stabilize the training. Since the LSUN scene datasets come in the Authors Info & Claims. In this figure, we use higher temperatures (t {0.6, 0.7, 0.8, 0.9}), but . Warming-up the KL Term: Similar to the previous work, we warm-up the KL term at the beginning of training[sonderby2016ladder]. But, it differs from IAF-VAEs in terms of i) neural networks implementing these models, ii) the parameterization of approximate posteriors, and iii) scaling up the training to large images. NVAE outperforms previous non-autoregressive models on most datasets and reduces the gap with autoregressive models. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Work fast with our official CLI. Reduce the number of groups: You can make NVAE smaller by using a smaller number of latent variable groups. We show that NVAE achieves state-of-the-art results among non-autoregressive likelihood-based models on the MNIST, CIFAR-10, CelebA 64, and CelebA HQ datasets and it provides a strong baseline on FFHQ. To the best of our knowledge, NVAE is the first successful application of VAEs to images as large as 256256 pixels. 3(b). sensitive to the encoder overfitting (see also Fig. Since the true posterior p(z|x) is in general intractable, the generative model is trained with the aid of an approximate posterior distribution or encoder q(z|x). Among them, VAEs have the advantage of fast and tractable sampling and easy-to-access encoding networks. Beginning of dialog window. To bound KL, we need to ensure that the encoder output does not change dramatically as its input changes. in each scale of latent variables. The objective is trained using the reparameterization trickkingma2014vae; rezende2014stochastic. 3(a). If nothing happens, download Xcode and try again. First, VAEs maximize the mutual information between the input and latent variablesbarber2004IM; alemi2016info, requiring the networks to retain the information content of the input data as much as possible. Among them, VAEs have the advantage of fast and tractable sampling and easy-to-access encoding networks. B.5 in the appendix for an experiment, stabilized by SR). Online: 15 April 2022 Publication History. However, VQ-VAEs objective differs substantially from VAEs and does not correspond to a lower bound on data log-likelihood. Thus, in the long run, enabling VAEs to generate high-quality images will help us reduce bias in the generated content, produce diverse output, and represent minorities better. This multi-scale approach enables NVAE to capture global long-range correlations at the top of the hierarchy and local fine-grained dependencies at the lower groups. 4.1, qualitative results in Sec. We encourage the interested readers to check the video in the supplementary material that visualizes a random walk in the latent space of NVAE. 2 and residual cells shown in Fig. The additional BN computation does not change the training time significantly, but it results in another 18% reduction in memory usage for our model on CIFAR-10. NVAE: A Deep Hierarchical Variational Autoencoder. However, it does improve the KL term by 0.04 bpd in training, and the final test log-likelihood by 0.03 bpd (see Sec. We show that NVAE achieves state-of-the-art results among non . 4.3. To keep matters simple, we use the hierarchical structure from IAF-VAEs, and we focus on carefully designing the neural networks. --num_channels_enc 192 --num_channels_dec 192 --epochs 45 --num_postprocess_cells 2 --num_preprocess_cells 2 \, --num_latent_scales 1 --num_latent_per_group 20 --num_cell_per_cond_enc 2 --num_cell_per_cond_dec 2 \, --num_preprocess_blocks 1 --num_postprocess_blocks 1 --num_groups_per_scale 28 \, --batch_size 24 --num_nf 1 --warmup_epochs 1 \, --weight_decay_norm 1e-2 --weight_decay_norm_anneal --weight_decay_norm_init 1e0 \, --num_process_per_node 8 --use_se --res_dist \, --fast_adamax --node_rank $NODE_RANK --num_proc_node 3 --master_address $IP_ADDR, --num_channels_enc 30 --num_channels_dec 30 --epochs 300 --num_postprocess_cells 2 --num_preprocess_cells 2 \, --num_latent_scales 5 --num_latent_per_group 20 --num_cell_per_cond_enc 2 --num_cell_per_cond_dec 2 \, --num_preprocess_blocks 1 --num_postprocess_blocks 1 --weight_decay_norm 1e-2 --num_groups_per_scale 16 \, --batch_size 4 --num_nf 2 --ada_groups --min_groups_per_scale 4 \, --weight_decay_norm_anneal --weight_decay_norm_init 1. Learn more. More Expressive Approximate Posteriors with Normalizing Flows: A tag already exists with the provided branch name. We propose Nouveau VAE (NVAE), a deep hierar- chical VAE built for image generation using depth-wise separable convolutions and batch normalization. In our early experiments, we empirically observed that depthwise convolutions outperform regular convolutions while keeping the number of parameters and the computational complexity orders of magnitudes smaller111A kk regular convolution, mapping a C-channel tensor to the same size, has k2C2 parameters and computational complexity of O(k2C2) per spatial location, whereas a depthwise convolution operating in the same regime has k2C parameters and O(k2C) complexity per location.. We present the main quantitative results in Sec. Finally, due to the unbounded KullbackLeibler (KL) divergence in the variational lower bound, training very deep hierarchical VAEs is often unstable. December 2020 Cite arXiv Website Type. Sign up to our mailing list for occasional updates. The main building block of our network is depthwise convolutionsvanhoucke2014depth; chollet2017xception that rapidly increase the receptive field of the network without dramatically increasing the number of parameters. As we can see, the residual distribution does virtually not change the number of active latent variables or reconstruction loss. Arash Vahdat, Jan Kautz. An unofficial toy implementation for NVAE A Deep Hierarchical Variational Autoencoder. number of channels with every spatial downsampling layer in the bottom-up network, and we double the number of Review 3. A summary of hyperparameters used in training NVAE with additional information. A Deep Hierarchical Variational Autoencoder, Discretized mix logistic distribution (Replaced with lighter Adaptive Loss). In most of our experiments, we are using We can write the variational lower bound LVAE(x) on logp(x) as: where q(z

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