Remote Inference over Dynamic Links via Adaptive Rate Deep Task-Oriented Vector Quantization

• URL: http://arxiv.org/abs/2501.02521v1
• Date: Sun, 05 Jan 2025 12:38:13 GMT
• Title: Remote Inference over Dynamic Links via Adaptive Rate Deep Task-Oriented Vector Quantization
• Authors: Eyal Fishel, May Malka, Shai Ginzach, Nir Shlezinger,
• Abstract summary: We propose Adaptive Rate Task-Oriented Vector Quantization (ARTOVeQ), a learned compression mechanism that is tailored for remote inference over dynamic links. We show that ARTOVeQ extends to support low-latency inference that is gradually refined via successive refinement principles. Numerical results demonstrate that the proposed scheme yields remote deep inference that operates with multiple rates, supports a broad range of bit budgets, and facilitates rapid inference that gradually improves with more bits exchanged.
• Score: 24.064287427162345
• License:
• Abstract: A broad range of technologies rely on remote inference, wherein data acquired is conveyed over a communication channel for inference in a remote server. Communication between the participating entities is often carried out over rate-limited channels, necessitating data compression for reducing latency. While deep learning facilitates joint design of the compression mapping along with encoding and inference rules, existing learned compression mechanisms are static, and struggle in adapting their resolution to changes in channel conditions and to dynamic links. To address this, we propose Adaptive Rate Task-Oriented Vector Quantization (ARTOVeQ), a learned compression mechanism that is tailored for remote inference over dynamic links. ARTOVeQ is based on designing nested codebooks along with a learning algorithm employing progressive learning. We show that ARTOVeQ extends to support low-latency inference that is gradually refined via successive refinement principles, and that it enables the simultaneous usage of multiple resolutions when conveying high-dimensional data. Numerical results demonstrate that the proposed scheme yields remote deep inference that operates with multiple rates, supports a broad range of bit budgets, and facilitates rapid inference that gradually improves with more bits exchanged, while approaching the performance of single-rate deep quantization methods.

Related papers

• Causal Context Adjustment Loss for Learned Image Compression [72.7300229848778]
  In recent years, learned image compression (LIC) technologies have surpassed conventional methods notably in terms of rate-distortion (RD) performance. Most present techniques are VAE-based with an autoregressive entropy model, which obviously promotes the RD performance by utilizing the decoded causal context. In this paper, we make the first attempt in investigating the way to explicitly adjust the causal context with our proposed Causal Context Adjustment loss.
  arXiv  Detail & Related papers  (2024-10-07T09:08:32Z)
• Differentiable Resolution Compression and Alignment for Efficient Video Classification and Retrieval [16.497758750494537]
  We propose an efficient video representation network with Differentiable Resolution Compression and Alignment mechanism. We leverage a Differentiable Context-aware Compression Module to encode the saliency and non-saliency frame features. We introduce a new Resolution-Align Transformer Layer to capture global temporal correlations among frame features with different resolutions.
  arXiv  Detail & Related papers  (2023-09-15T05:31:53Z)
• 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)
• Wideband and Entropy-Aware Deep Soft Bit Quantization [1.7259824817932292]
  We introduce a novel deep learning solution for soft bit quantization across wideband channels. Our method is trained end-to-end with quantization- and entropy-aware augmentations to the loss function. Our method achieves a compression gain of up to $10 %$ in the high SNR regime versus previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-10-18T18:00:05Z)
• Remote Multilinear Compressive Learning with Adaptive Compression [107.87219371697063]
  MultiIoT Compressive Learning (MCL) is an efficient signal acquisition and learning paradigm for multidimensional signals. We propose a novel optimization scheme that enables such a feature for MCL models.
  arXiv  Detail & Related papers  (2021-09-02T19:24:03Z)
• CCVS: Context-aware Controllable Video Synthesis [95.22008742695772]
  presentation introduces a self-supervised learning approach to the synthesis of new video clips from old ones. It conditions the synthesis process on contextual information for temporal continuity and ancillary information for fine control.
  arXiv  Detail & Related papers  (2021-07-16T17:57:44Z)
• Efficient Distributed Auto-Differentiation [22.192220404846267]
  gradient-based algorithms for training large deep neural networks (DNNs) are communication-heavy. We introduce a surprisingly simple statistic for training distributed DNNs that is more communication-friendly than the gradient. The process provides the flexibility of averaging gradients during backpropagation, enabling novel flexible training schemas.
  arXiv  Detail & Related papers  (2021-02-18T21:46:27Z)
• Learning Task-Oriented Communication for Edge Inference: An Information Bottleneck Approach [3.983055670167878]
  A low-end edge device transmits the extracted feature vector of a local data sample to a powerful edge server for processing. It is critical to encode the data into an informative and compact representation for low-latency inference given the limited bandwidth. We propose a learning-based communication scheme that jointly optimize feature extraction, source coding, and channel coding.
  arXiv  Detail & Related papers  (2021-02-08T12:53:32Z)
• Solving Sparse Linear Inverse Problems in Communication Systems: A Deep Learning Approach With Adaptive Depth [51.40441097625201]
  We propose an end-to-end trainable deep learning architecture for sparse signal recovery problems. The proposed method learns how many layers to execute to emit an output, and the network depth is dynamically adjusted for each task in the inference phase.
  arXiv  Detail & Related papers  (2020-10-29T06:32:53Z)
• A Variational Information Bottleneck Based Method to Compress Sequential Networks for Human Action Recognition [9.414818018857316]
  We propose a method to effectively compress Recurrent Neural Networks (RNNs) used for Human Action Recognition (HAR) We use a Variational Information Bottleneck (VIB) theory-based pruning approach to limit the information flow through the sequential cells of RNNs to a small subset. We combine our pruning method with a specific group-lasso regularization technique that significantly improves compression. It is shown that our method achieves over 70 times greater compression than the nearest competitor with comparable accuracy for the task of action recognition on UCF11.
  arXiv  Detail & Related papers  (2020-10-03T12:41:51Z)
• PowerGossip: Practical Low-Rank Communication Compression in Decentralized Deep Learning [62.440827696638664]
  We introduce a simple algorithm that directly compresses the model differences between neighboring workers. Inspired by the PowerSGD for centralized deep learning, this algorithm uses power steps to maximize the information transferred per bit.
  arXiv  Detail & Related papers  (2020-08-04T09:14:52Z)

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.