Understanding Vector Quantization in VQ-VAE

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Initialize the Layer Flattening for Flexibility The Distance Computation Selecting the Closest Codebook Embedding Quantization and Reshaping Loss and Gradient Flow Bringing it together The Vector Quantized Variational Autoencoder (VQ-VAE) leverages a unique mechanism called vector quantization to map continuous latent representations into discrete embeddings. In this article, I will try explaining the mechanism in a more hands on way.

Initialize the Layer

class VQEmbedding(nn.Module):

def __init__(self, num_embeddings, embedding_dim):