Value Iteration Networks with Gated Summarization Module

• URL: http://arxiv.org/abs/2305.07039v2
• Date: Tue, 16 May 2023 12:41:52 GMT
• Title: Value Iteration Networks with Gated Summarization Module
• Authors: Jinyu Cai, Jialong Li, Mingyue Zhang and Kenji Tei
• Abstract summary: We address the challenges faced by Value Iteration Networks (VIN) in handling larger input maps and mitigating the impact of accumulated errors caused by increased iterations. We propose a novel approach, Value Iteration Networks with Gated Summarization Module (GS-VIN), which incorporates two main improvements.
• Score: 7.289178621436725
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In this paper, we address the challenges faced by Value Iteration Networks (VIN) in handling larger input maps and mitigating the impact of accumulated errors caused by increased iterations. We propose a novel approach, Value Iteration Networks with Gated Summarization Module (GS-VIN), which incorporates two main improvements: (1) employing an Adaptive Iteration Strategy in the Value Iteration module to reduce the number of iterations, and (2) introducing a Gated Summarization module to summarize the iterative process. The adaptive iteration strategy uses larger convolution kernels with fewer iteration times, reducing network depth and increasing training stability while maintaining the accuracy of the planning process. The gated summarization module enables the network to emphasize the entire planning process, rather than solely relying on the final global planning outcome, by temporally and spatially resampling the entire planning process within the VI module. We conduct experiments on 2D grid world path-finding problems and the Atari Mr. Pac-man environment, demonstrating that GS-VIN outperforms the baseline in terms of single-step accuracy, planning success rate, and overall performance across different map sizes. Additionally, we provide an analysis of the relationship between input size, kernel size, and the number of iterations in VI-based models, which is applicable to a majority of VI-based models and offers valuable insights for researchers and industrial deployment.

Related papers

• Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression [83.27791109672927]
  We show how a state-augmented graph neural network (GNN) parametrization for the resource allocation policy circumvents the drawbacks of the ubiquitous dual subgradient methods.<n>Lagrangian maximizing state-augmented policies are learned during the offline training phase.<n>We prove a convergence result and an exponential probability bound on the excursions of the dual function (iterate) optimality gaps.
  arXiv  Detail & Related papers  (2025-06-23T15:20:58Z)
• DP-LET: An Efficient Spatio-Temporal Network Traffic Prediction Framework [13.65226228907662]
  DP-LET is an efficient feature-temporal network traffic prediction framework. DP-LET consists of a data processing module, a local feature enhancement module, and a Transformer-based prediction module. A real-world cellular traffic prediction demonstrates the practicality of DP-LET.
  arXiv  Detail & Related papers  (2025-04-04T02:52:43Z)
• BHViT: Binarized Hybrid Vision Transformer [53.38894971164072]
  Model binarization has made significant progress in enabling real-time and energy-efficient computation for convolutional neural networks (CNN) We propose BHViT, a binarization-friendly hybrid ViT architecture and its full binarization model with the guidance of three important observations. Our proposed algorithm achieves SOTA performance among binary ViT methods.
  arXiv  Detail & Related papers  (2025-03-04T08:35:01Z)
• LiNeS: Post-training Layer Scaling Prevents Forgetting and Enhances Model Merging [80.17238673443127]
  LiNeS is a post-training editing technique designed to preserve pre-trained generalization while enhancing fine-tuned task performance. LiNeS demonstrates significant improvements in both single-task and multi-task settings across various benchmarks in vision and natural language processing.
  arXiv  Detail & Related papers  (2024-10-22T16:26:05Z)
• Optimization of geological carbon storage operations with multimodal latent dynamic model and deep reinforcement learning [1.8549313085249324]
  This study introduces the multimodal latent dynamic (MLD) model, a deep learning framework for fast flow prediction and well control optimization in GCS. Unlike existing models, the MLD supports diverse input modalities, allowing comprehensive data interactions. The approach outperforms traditional methods, achieving the highest NPV while reducing computational resources by over 60%.
  arXiv  Detail & Related papers  (2024-06-07T01:30:21Z)
• StyDeSty: Min-Max Stylization and Destylization for Single Domain Generalization [85.18995948334592]
  Single domain generalization (single DG) aims at learning a robust model generalizable to unseen domains from only one training domain. State-of-the-art approaches have mostly relied on data augmentations, such as adversarial perturbation and style enhancement, to synthesize new data. We propose emphStyDeSty, which explicitly accounts for the alignment of the source and pseudo domains in the process of data augmentation.
  arXiv  Detail & Related papers  (2024-06-01T02:41:34Z)
• SSGA-Net: Stepwise Spatial Global-local Aggregation Networks for for Autonomous Driving [27.731481134782577]
  Current models usually aggregate features from the neighboring frames to enhance the object representations for the task heads. These methods rely on the information from the future frames and suffer from high computational complexity. We introduce a stepwise spatial global-local aggregation network to solve these problems.
  arXiv  Detail & Related papers  (2024-05-29T08:12:51Z)
• Unleashing Network Potentials for Semantic Scene Completion [50.95486458217653]
  This paper proposes a novel SSC framework - Adrial Modality Modulation Network (AMMNet) AMMNet introduces two core modules: a cross-modal modulation enabling the interdependence of gradient flows between modalities, and a customized adversarial training scheme leveraging dynamic gradient competition. Extensive experimental results demonstrate that AMMNet outperforms state-of-the-art SSC methods by a large margin.
  arXiv  Detail & Related papers  (2024-03-12T11:48:49Z)
• Optimization Guarantees of Unfolded ISTA and ADMM Networks With Smooth Soft-Thresholding [57.71603937699949]
  We study optimization guarantees, i.e., achieving near-zero training loss with the increase in the number of learning epochs. We show that the threshold on the number of training samples increases with the increase in the network width.
  arXiv  Detail & Related papers  (2023-09-12T13:03:47Z)
• Variational Density Propagation Continual Learning [0.0]
  Deep Neural Networks (DNNs) deployed to the real world are regularly subject to out-of-distribution (OoD) data. This paper proposes a framework for adapting to data distribution drift modeled by benchmark Continual Learning datasets.
  arXiv  Detail & Related papers  (2023-08-22T21:51:39Z)
• Local-Global Transformer Enhanced Unfolding Network for Pan-sharpening [13.593522290577512]
  Pan-sharpening aims to increase the spatial resolution of the low-resolution multispectral (LrMS) image with the guidance of the corresponding panchromatic (PAN) image. Although deep learning (DL)-based pan-sharpening methods have achieved promising performance, most of them have a two-fold deficiency.
  arXiv  Detail & Related papers  (2023-04-28T03:34:36Z)
• InverseForm: A Loss Function for Structured Boundary-Aware Segmentation [80.39674800972182]
  We present a novel boundary-aware loss term for semantic segmentation using an inverse-transformation network. This plug-in loss term complements the cross-entropy loss in capturing boundary transformations. We analyze the quantitative and qualitative effects of our loss function on three indoor and outdoor segmentation benchmarks.
  arXiv  Detail & Related papers  (2021-04-06T18:52:45Z)
• OverNet: Lightweight Multi-Scale Super-Resolution with Overscaling Network [3.6683231417848283]
  We introduce OverNet, a deep but lightweight convolutional network to solve SISR at arbitrary scale factors with a single model. We show that our network outperforms previous state-of-the-art results in standard benchmarks while using fewer parameters than previous approaches.
  arXiv  Detail & Related papers  (2020-08-05T22:10:29Z)
• Crowd Counting via Hierarchical Scale Recalibration Network [61.09833400167511]
  We propose a novel Hierarchical Scale Recalibration Network (HSRNet) to tackle the task of crowd counting. HSRNet models rich contextual dependencies and recalibrating multiple scale-associated information. Our approach can ignore various noises selectively and focus on appropriate crowd scales automatically.
  arXiv  Detail & Related papers  (2020-03-07T10:06:47Z)

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.