HyperVQ: MLR-based Vector Quantization in Hyperbolic Space

• URL: http://arxiv.org/abs/2403.13015v1
• Date: Mon, 18 Mar 2024 03:17:08 GMT
• Title: HyperVQ: MLR-based Vector Quantization in Hyperbolic Space
• Authors: Nabarun Goswami, Yusuke Mukuta, Tatsuya Harada,
• Abstract summary: We study the use of hyperbolic spaces for vector quantization (HyperVQ) We show that hyperVQ performs comparably in reconstruction and generative tasks while outperforming VQ in discriminative tasks and learning a highly disentangled latent space.
• Score: 56.4245885674567
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The success of models operating on tokenized data has led to an increased demand for effective tokenization methods, particularly when applied to vision or auditory tasks, which inherently involve non-discrete data. One of the most popular tokenization methods is Vector Quantization (VQ), a key component of several recent state-of-the-art methods across various domains. Typically, a VQ Variational Autoencoder (VQVAE) is trained to transform data to and from its tokenized representation. However, since the VQVAE is trained with a reconstruction objective, there is no constraint for the embeddings to be well disentangled, a crucial aspect for using them in discriminative tasks. Recently, several works have demonstrated the benefits of utilizing hyperbolic spaces for representation learning. Hyperbolic spaces induce compact latent representations due to their exponential volume growth and inherent ability to model hierarchical and structured data. In this work, we explore the use of hyperbolic spaces for vector quantization (HyperVQ), formulating the VQ operation as a hyperbolic Multinomial Logistic Regression (MLR) problem, in contrast to the Euclidean K-Means clustering used in VQVAE. Through extensive experiments, we demonstrate that hyperVQ performs comparably in reconstruction and generative tasks while outperforming VQ in discriminative tasks and learning a highly disentangled latent space.

Related papers

• HRVMamba: High-Resolution Visual State Space Model for Dense Prediction [60.80423207808076]
  State Space Models (SSMs) with efficient hardware-aware designs have demonstrated significant potential in computer vision tasks. These models have been constrained by three key challenges: insufficient inductive bias, long-range forgetting, and low-resolution output representation. We introduce the Dynamic Visual State Space (DVSS) block, which employs deformable convolution to mitigate the long-range forgetting problem. We also introduce High-Resolution Visual State Space Model (HRVMamba) based on the DVSS block, which preserves high-resolution representations throughout the entire process.
  arXiv  Detail & Related papers  (2024-10-04T06:19:29Z)
• LASERS: LAtent Space Encoding for Representations with Sparsity for Generative Modeling [3.9426000822656224]
  We show that our more latent space is more expressive and has leads to better representations than the Vector Quantization approach. Our results thus suggest that the true benefit of the VQ approach might not be from discretization of the latent space, but rather the lossy compression of the latent space.
  arXiv  Detail & Related papers  (2024-09-16T08:20:58Z)
• LongVQ: Long Sequence Modeling with Vector Quantization on Structured Memory [63.41820940103348]
  Self-attention mechanism's computational cost limits its practicality for long sequences. We propose a new method called LongVQ to compress the global abstraction as a length-fixed codebook. LongVQ effectively maintains dynamic global and local patterns, which helps to complement the lack of long-range dependency issues.
  arXiv  Detail & Related papers  (2024-04-17T08:26:34Z)
• LL-VQ-VAE: Learnable Lattice Vector-Quantization For Efficient Representations [0.0]
  We introduce learnable lattice vector quantization and demonstrate its effectiveness for learning discrete representations. Our method, termed LL-VQ-VAE, replaces the vector quantization layer in VQ-VAE with lattice-based discretization. Compared to VQ-VAE, our method obtains lower reconstruction errors under the same training conditions, trains in a fraction of the time, and with a constant number of parameters.
  arXiv  Detail & Related papers  (2023-10-13T20:03:18Z)
• Vector Quantized Wasserstein Auto-Encoder [57.29764749855623]
  We study learning deep discrete representations from the generative viewpoint. We endow discrete distributions over sequences of codewords and learn a deterministic decoder that transports the distribution over the sequences of codewords to the data distribution. We develop further theories to connect it with the clustering viewpoint of WS distance, allowing us to have a better and more controllable clustering solution.
  arXiv  Detail & Related papers  (2023-02-12T13:51:36Z)
• Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization [76.68866368409216]
  We propose learning to dynamically select discretization tightness conditioned on inputs. We show that dynamically varying tightness in communication bottlenecks can improve model performance on visual reasoning and reinforcement learning tasks.
  arXiv  Detail & Related papers  (2022-02-02T23:54:26Z)
• Unshuffling Data for Improved Generalization [65.57124325257409]
  Generalization beyond the training distribution is a core challenge in machine learning. We show that partitioning the data into well-chosen, non-i.i.d. subsets treated as multiple training environments can guide the learning of models with better out-of-distribution generalization.
  arXiv  Detail & Related papers  (2020-02-27T03:07:41Z)
• Accuracy vs. Complexity: A Trade-off in Visual Question Answering Models [39.338304913058685]
  We study the trade-off between the model complexity and the performance on the Visual Question Answering task. We focus on the effect of "multi-modal fusion" in VQA models that is typically the most expensive step in a VQA pipeline.
  arXiv  Detail & Related papers  (2020-01-20T11:27:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.