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Journal Club: VQ-VAE2

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Takuya Koumura

Material of the journal club. Topic: VQ-VAE2

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Vector Quantized Variational Auto Encoder 2 (VQ-VAE2) Takuya KOUMURA 2019.07.05 Journal club
2019.07.05 Takuya KOUMURA cycentum.com p. 1 Results 1024×1024 256×256
2019.07.05 Takuya KOUMURA cycentum.com p. 2 Probabilistic generative model x ~ P(x) x ∈ RN (eg: pixel values, sound amplitudes) Difficult to sample because xis are highly dependent on each other and N is large x P(x) Sample 3 classes of generative models ⚫Generative adversarial network (GAN) ⚫Variational auto encoder (VAE) ⚫Discrete autoregressive model
2019.07.05 Takuya KOUMURA cycentum.com p. 3 History PixelRNN & PixelCNN Oord A van den, Kalchbrenner N, Kavukcuoglu K (2016) Pixel Recurrent Neural Networks VQ-VAE2 Razavi A, Oord A van den, Vinyals O (2019) Generating Diverse High- Fidelity Images with VQ-VAE-2 VQ-VAE Oord A van den, Vinyals O, Kavukcuoglu K (2017) Neural Discrete Representation Learning VAE Kingma DP, Welling M (2013) Auto- Encoding Variational Bayes. WaveNet Oord A van den, Dieleman S, Zen H, Simonyan K, Vinyals O, Graves A, Kalchbrenner N, Senior A, Kavukcuoglu K (2016) WaveNet: A Generative Model for Raw Audio
2019.07.05 Takuya KOUMURA cycentum.com p. 4 History PixelRNN & PixelCNN Oord A van den, Kalchbrenner N, Kavukcuoglu K (2016) Pixel Recurrent Neural Networks VQ-VAE2 Razavi A, Oord A van den, Vinyals O (2019) Generating Diverse High- Fidelity Images with VQ-VAE-2 VQ-VAE Oord A van den, Vinyals O, Kavukcuoglu K (2017) Neural Discrete Representation Learning VAE Kingma DP, Welling M (2013) Auto- Encoding Variational Bayes. WaveNet Oord A van den, Dieleman S, Zen H, Simonyan K, Vinyals O, Graves A, Kalchbrenner N, Senior A, Kavukcuoglu K (2016) WaveNet: A Generative Model for Raw Audio
2019.07.05 Takuya KOUMURA cycentum.com p. 5 Discrete autoregressive model P(x) = ΠiP(xi | x1, …, xi−1) The relationship of a pixel to the other pixels are modeled by a conditional probability NN NN
2019.07.05 Takuya KOUMURA cycentum.com p. 6 Discrete autoregressive model P(x) = ΠiP(xi | x1, …, xi−1) The relationship of a pixel to the other pixels are modeled by a conditional probability NN
2019.07.05 Takuya KOUMURA cycentum.com p. 7 Discrete autoregressive model xi ~ P(xi | x1, …, xi−1) xi is discrete The output layer is softmax ⚫In the case of a RGB image ⚪ The task is 256-way classification ⚪ P(xi) = P(xiR | x<i)P(xiG | xiR, x<i)P(xiB | xiR, xiG, x<i) ⚫In the case of a sound ⚪ µ-law companding transformation ⚪ Quantizing to 256 discrete values
2019.07.05 Takuya KOUMURA cycentum.com p. 8 Results 64×64 WaveNet: https://deepmind.com/blog/wavenet-generative-model-raw-audio/
2019.07.05 Takuya KOUMURA cycentum.com p. 9 Variational Auto Encoder z ∈ RD : A latent variable z ~ P(z)： A simple distribution, easy to sample from (eg: Gaussian, uniform, or discrete) x = f(z) : Deterministic mapping from a latent to the data → can be modeled by an NN x NN z P(z) Sample
2019.07.05 Takuya KOUMURA cycentum.com p. 10 Variational Auto Encoder z = E(x)： Encoder x = D(z)： Decoder During training, z is guided to follow a simple distribution, such as Gaussian, uniform, or discrete zP(z) x NN z P(z) x NN SampleGuide to follow DecoderEncoder
2019.07.05 Takuya KOUMURA cycentum.com p. 11 Training Loss = Error(x, xgen) + KLD(p(z|x) || Standard Gaussian) zP(z) xgen NN z P(z) x NN SampleGuide to follow DecoderEncoder
2019.07.05 Takuya KOUMURA cycentum.com p. 12 Variational Auto Encoder z = E(x)： Encoder x = D(z)： Decoder During training, z is guided to follow a simple distribution, such as Gaussian, uniform, or discrete zP(z) x NN z P(z) x NN SampleGuide to follow DecoderEncoder
2019.07.05 Takuya KOUMURA cycentum.com p. 13 Rationale “we concentrate on discrete representations which are potentially a more natural fit for many of the modalities we are interested in. Language is inherently discrete, similarly speech is typically represented as a sequence of symbols. Images can often be described concisely by language. Furthermore, discrete representations are a natural fit for complex reasoning, planning and predictive learning (e.g., if it rains, I will use an umbrella).”
2019.07.05 Takuya KOUMURA cycentum.com p. 14 VQ-VAE ze x NN zq x NN DecoderEncoder zq Discretize
2019.07.05 Takuya KOUMURA cycentum.com p. 15 Discretization ⚫ze: output of the encoder (continuous) ⚫ek: embedding vectors (k ∈ 1, ..., K) ⚫zq: the nearest ek from ze Embedding vectors
2019.07.05 Takuya KOUMURA cycentum.com p. 16 Training Reconstruction error Move ek closer to ze Alternatively, ek can be a moving average of ze Move ze closer to ek
2019.07.05 Takuya KOUMURA cycentum.com p. 17 Prior ze x D zq x E Discretize k zq = ek ⚫During training, elements in z are assumed to be independent
2019.07.05 Takuya KOUMURA cycentum.com p. 18 Prior ⚫During training, elements in z are assumed to be independent ⚫After training, a prior over z is modeled by an autoregressive model, from which z is sampled during generation ze x D zq x E Discretize NN zq = ek Auto regressive model
2019.07.05 Takuya KOUMURA cycentum.com p. 19 Results ⚫Images 128×128 ⚫Videos ⚫Audio https://avdnoord.github.io/ homepage/vqvae/
2019.07.05 Takuya KOUMURA cycentum.com p. 20 VQ-VAE2 Hierarchical discrete latents So far, only for images
2019.07.05 Takuya KOUMURA cycentum.com p. 21 Neural network architecture ⚫A stack of residual connections and skip connections ⚫Sometimes with gating operation

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