Rapid Network Adaptation: Learning to Adapt Neural Networks Using Test-Time Feedback

• URL: http://arxiv.org/abs/2309.15762v1
• Date: Wed, 27 Sep 2023 16:20:39 GMT
• Title: Rapid Network Adaptation: Learning to Adapt Neural Networks Using Test-Time Feedback
• Authors: Teresa Yeo, O\u{g}uzhan Fatih Kar, Zahra Sodagar, Amir Zamir
• Abstract summary: We create a closed-loop system that makes use of a test-time feedback signal to adapt a network on the fly. We show that this loop can be effectively implemented using a learning-based function, which realizes an amortized for the network. This leads to an adaptation method, named Rapid Network Adaptation (RNA), that is notably more flexible and orders of magnitude faster than the baselines.
• Score: 12.946419909506883
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a method for adapting neural networks to distribution shifts at test-time. In contrast to training-time robustness mechanisms that attempt to anticipate and counter the shift, we create a closed-loop system and make use of a test-time feedback signal to adapt a network on the fly. We show that this loop can be effectively implemented using a learning-based function, which realizes an amortized optimizer for the network. This leads to an adaptation method, named Rapid Network Adaptation (RNA), that is notably more flexible and orders of magnitude faster than the baselines. Through a broad set of experiments using various adaptation signals and target tasks, we study the efficiency and flexibility of this method. We perform the evaluations using various datasets (Taskonomy, Replica, ScanNet, Hypersim, COCO, ImageNet), tasks (depth, optical flow, semantic segmentation, classification), and distribution shifts (Cross-datasets, 2D and 3D Common Corruptions) with promising results. We end with a discussion on general formulations for handling distribution shifts and our observations from comparing with similar approaches from other domains.

Related papers

• Dual-Path Adversarial Lifting for Domain Shift Correction in Online Test-time Adaptation [59.18151483767509]
  We introduce a dual-path token lifting for domain shift correction in test time adaptation. We then perform dual-path lifting with interleaved token prediction and update between the path of domain shift tokens and the path of class tokens. Experimental results on the benchmark datasets demonstrate that our proposed method significantly improves the online fully test-time domain adaptation performance.
  arXiv  Detail & Related papers  (2024-08-26T02:33:47Z)
• Learning Fast and Slow for Online Time Series Forecasting [76.50127663309604]
  Fast and Slow learning Networks (FSNet) is a holistic framework for online time-series forecasting. FSNet balances fast adaptation to recent changes and retrieving similar old knowledge. Our code will be made publicly available.
  arXiv  Detail & Related papers  (2022-02-23T18:23:07Z)
• Unsupervised Domain-adaptive Hash for Networks [81.49184987430333]
  Domain-adaptive hash learning has enjoyed considerable success in the computer vision community. We develop an unsupervised domain-adaptive hash learning method for networks, dubbed UDAH.
  arXiv  Detail & Related papers  (2021-08-20T12:09:38Z)
• Decoupled Greedy Learning of CNNs for Synchronous and Asynchronous Distributed Learning [3.7722254371820987]
  We consider a simple alternative based on minimal feedback, which we call Decoupled Greedy Learning (DGL) It is based on a classic greedy relaxation of the joint training objective, recently shown to be effective in the context of Convolutional Neural Networks (CNNs) on large-scale image classification. We show theoretically and empirically that this approach converges and compare it to the sequential solvers.
  arXiv  Detail & Related papers  (2021-06-11T13:55:17Z)
• A Deep Value-network Based Approach for Multi-Driver Order Dispatching [55.36656442934531]
  We propose a deep reinforcement learning based solution for order dispatching. We conduct large scale online A/B tests on DiDi's ride-dispatching platform. Results show that CVNet consistently outperforms other recently proposed dispatching methods.
  arXiv  Detail & Related papers  (2021-06-08T16:27:04Z)
• Fast Adaptation with Linearized Neural Networks [35.43406281230279]
  We study the inductive biases of linearizations of neural networks, which we show to be surprisingly good summaries of the full network functions. Inspired by this finding, we propose a technique for embedding these inductive biases into Gaussian processes through a kernel designed from the Jacobian of the network. In this setting, domain adaptation takes the form of interpretable posterior inference, with accompanying uncertainty estimation.
  arXiv  Detail & Related papers  (2021-03-02T03:23:03Z)
• Local Critic Training for Model-Parallel Learning of Deep Neural Networks [94.69202357137452]
  We propose a novel model-parallel learning method, called local critic training. We show that the proposed approach successfully decouples the update process of the layer groups for both convolutional neural networks (CNNs) and recurrent neural networks (RNNs) We also show that trained networks by the proposed method can be used for structural optimization.
  arXiv  Detail & Related papers  (2021-02-03T09:30:45Z)
• Network Adjustment: Channel Search Guided by FLOPs Utilization Ratio [101.84651388520584]
  This paper presents a new framework named network adjustment, which considers network accuracy as a function of FLOPs. Experiments on standard image classification datasets and a wide range of base networks demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2020-04-06T15:51:00Z)

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.