resolution of latent variables will have --num_groups_per_scale groups and Intro & Overview NVAE: A Deep Hierarchical Variational Autoencoder (Paper Explained) 28,043 views Jul 9, 2020 VAEs have been traditionally hard to train at high resolutions and unstable when. To avoid this, similar to BigGAN, we readjust running mean and standard deviation in the BN layers by sampling from the generative model 500 times for the given temperature, and then we use the readjusted statistics for the final sampling444This intriguing effect of BN on VAEs and GANs requires further study in future work. B.5 in the appendix for an experiment, stabilized by SR). In the commands above, we are constructing big NVAE models that require several days of training If you modify the settings Neural Information Processing Systems (NeurIPS) is a multi-track machine learning and computational neuroscience conference that includes invited talks, demonstrations, symposia and oral and poster presentations of refereed papers. Here, the baseline without residual distribution corresponds to the parameterization used in IAF-VAEkingma2016improved. Hyperparameters: Given a large number of datasets and the heavy compute requirements, we do not exhaustively optimize the hyperparameters. Moreover, VQ-VAE-2 uses PixelCNNvan2016pixel in its prior for latent variables up to 128128 dims that can be very slow to sample from, while NVAE uses an unconditional decoder in the data space. You can reduce the number of residual cells to one to make the model 10 shows an experiment on the FFHQ dataset. NVAE is built in Python 3.7 using PyTorch 1.6.0. The role of neural network architectures for VAEs is somewhat overlooked, as most previous work borrows the architectures from classification tasks. ii) We propose a new residual parameterization of the approximate posteriors. 6 summarizes the hyperparameters used in our experiments. Warming-up the KL Term: Similar to the previous work, we warm-up the KL term at the beginning of training[sonderby2016ladder]. Authors: Arash Vahdat. Residual Cells: In Table3, we examine the cells in Fig3 for the bottom-up encoder and top-down generative models. Sec. arXiv as responsive web pages so you Optimization: For all the experiments, we use the AdaMax[kingma2014adam] optimizer for training with the initial learning rate of 0.01 and with cosine learning rate decay. To bound KL, we need to ensure that the encoder output does not change dramatically as its input changes. Summary and Contributions: The paper presents Nouveau VAE, a deep hierarchical VAE with a novel architecture consisting of 1. depthwise separabale convs to increase receptive field of generator without introducing lots of params, and batch norm, swish activation and squeeze excitation in architecture of residual block to further improve performance 2. stabilise optimisation of the KL . 2(b) for different numbers of groups (L). On CIFAR-10, NVAE improves the state-of-the-art from 2.98 to 2.91 bpd. However, depthwise convolutions have limited expressivity as they operate in each channel separately. NVAE borrows the statistical models (i.e., hierarchical prior and approximate posterior, etc.etc) from IAF-VAEs. We show that NVAE achieves state-of-the-art results among non-autoregressive likelihood-based models on the MNIST, CIFAR-10, and CelebA HQ datasets and it provides a strong baseline on FFHQ. (ii) A careful examination of the residual cells in Fig. This enables us to reduce the GPU memory by 40%. The results are reported in Table1. As we can see, the sampled images are not present in the training set. We show that NVAE achieves state-of-the-art results among non . If nothing happens, download GitHub Desktop and try again. The current state-of-the-art VAEskingma2016improved; maaloe2019biva omit batch normalization (BN)ioffe2015batch to combat the sources of randomness that could potentially amplify their instability. However, on Celeb-A HQ and FFHQ, we observe that training is initially unstable unless for {1,10} which applies a very strong smoothness. Recall that we halve the All the hyper-parameters are identical between the two runs. Since the order of latent variable groups are shared between q(z|x) and p(z), we also require an additional top-down network to infer latent variables group-by-group. 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). Scribd is the world's largest social reading and publishing site. Just specify the image directory, see more with python train.py -h. During training, the dataloader will capture the central area of the image and worse likelihood generates much better images in low temperature on this dataset. While running any of the commands above, you can monitor the training progress using Tensorboard: Above, $CHECKPOINT_DIR and $EXPR_ID are the same variables used for running the main training script. Visit this Google drive location and download While the majority of the research in VAEs is focused on the statistical challenges, we explore the orthogonal direction of carefully designing neural architectures for hierarchical VAEs. Recently, BIVAmaaloe2019biva showed state-of-the-art VAE results by extending bidirectional inference to latent variables. Below IP_ADDR is the IP address of the machine that will host the process with rank 0 We hypothesize that by regularizing the Lipschitz constant, we can ensure that the latent codes predicted by the encoder remain bounded, resulting in a stable KL minimization. Given the lower memory and faster training with regular cells, we use these cells for the bottom-up model and depthwise cells for the top-down model. Escape will cancel and close the window. normalizing flows are enabled. We provide checkpoints on MNIST, CIFAR-10, CelebA 64, CelebA HQ 256, FFHQ in We propose Nouveau VAE (NVAE), a deep hierarchical VAE built for image generation using depth-wise separable convolutions and batch normalization. 3(a). 24 32-GB V100 GPUs are used for training NVAE on FFHQ 256. NVAE is equipped with a residual parameterization of Normal distributions and its training is stabilized by spectral regularization. @article{Vahdat2020NVAEAD, title={NVAE: A Deep Hierarchical Variational Autoencoder}, author={Arash Vahdat and Jan Kautz}, journal={ArXiv}, year={2020}, volume . You signed in with another tab or window. We introduced residual parameterization of Normal distributions in the encoder and spectral regularization for stabilizing the training of very deep models. Training takes about 55 hours. Top retrieved images from the training set are visualized for samples generated by NVAE in each row. Normalizing flows, autoregressive models, variational autoencoders (VAEs), and deep energy-based models are among competing likelihood-based frameworks for deep generative learning. bias introduced in data collection will make VAEs generate samples with a similar bias. In our early experiments, a smaller model with 24 channels instead of 30, could be trained on only 8 GPUs in 2 and residual cells shown in Fig. This indicates that the network architecture is an important component in VAEs and a carefully designed network with Normal distributions in encoder can compensate for some of the statistical challenges. 2 is still unbounded. Close suggestions Search Search. A tag already exists with the provided branch name. Set --data to the same argument that was used when training NVAE (our example is for MNIST). The majority of the research efforts on improving VAEskingma2014vae; rezende2014stochastic is dedicated to the statistical challenges, such as reducing the gap between approximate and true posterior distributionsrezendeICML15Normalizing; kingma2016improved; gregor2015draw; cremer18amortization; marino18amortized; maaloe16auxiliary; ranganath16hierarchical; vahdat2019UndirectedPost, formulating tighter boundsburda2015importance; li2016renyi; bornschein2016bidirectional; masrani2019thermodynamic, reducing the gradient noiseroeder2017sticking; tucker2018doubly, extending VAEs to discrete variablesmaddison2016concrete; jang2016categorical; rolfe2016discrete; Vahdat2018DVAE++; vahdat2018dvaes; tucker2017rebar; grathwohl2017backpropagation, or tackling posterior collapsebowman2016generating; razavi2019collapse; gulrajani2016pixelvae; lucas2019collapse. We can write the variational lower bound LVAE(x) on logp(x) as: where q(z
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