Decorrelating Structure via Adapters Makes Ensemble Learning Practical for Semi-supervised Learning
- URL: http://arxiv.org/abs/2408.04150v1
- Date: Thu, 8 Aug 2024 01:31:38 GMT
- Title: Decorrelating Structure via Adapters Makes Ensemble Learning Practical for Semi-supervised Learning
- Authors: Jiaqi Wu, Junbiao Pang, Qingming Huang,
- Abstract summary: In computer vision, traditional ensemble learning methods exhibit either a low training efficiency or the limited performance.
We propose a lightweight, loss-function-free, and architecture-agnostic ensemble learning by the Decorrelating Structure via Adapters (DSA) for various visual tasks.
- Score: 50.868594148443215
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In computer vision, traditional ensemble learning methods exhibit either a low training efficiency or the limited performance to enhance the reliability of deep neural networks. In this paper, we propose a lightweight, loss-function-free, and architecture-agnostic ensemble learning by the Decorrelating Structure via Adapters (DSA) for various visual tasks. Concretely, the proposed DSA leverages the structure-diverse adapters to decorrelate multiple prediction heads without any tailed regularization or loss. This allows DSA to be easily extensible to architecture-agnostic networks for a range of computer vision tasks. Importantly, the theoretically analysis shows that the proposed DSA has a lower bias and variance than that of the single head based method (which is adopted by most of the state of art approaches). Consequently, the DSA makes deep networks reliable and robust for the various real-world challenges, \textit{e.g.}, data corruption, and label noises. Extensive experiments combining the proposed method with FreeMatch achieved the accuracy improvements of 5.35% on CIFAR-10 dataset with 40 labeled data and 0.71% on CIFAR-100 dataset with 400 labeled data. Besides, combining the proposed method with DualPose achieved the improvements in the Percentage of Correct Keypoints (PCK) by 2.08% on the Sniffing dataset with 100 data (30 labeled data), 5.2% on the FLIC dataset with 100 data (including 50 labeled data), and 2.35% on the LSP dataset with 200 data (100 labeled data).
Related papers
- Low-Quality Training Data Only? A Robust Framework for Detecting Encrypted Malicious Network Traffic [19.636282208765547]
When machine learning models are trained with low-quality training data, they suffer degraded performance.
We develop RAPIER that fully utilizes different distributions of normal and malicious traffic data in the feature space.
RAPIER effectively achieves encrypted malicious traffic detection with the best F1 score of 0.773 and improves the F1 score of existing methods by an average of 272.5%.
arXiv Detail & Related papers (2023-09-09T13:49:30Z) - Patch-Level Contrasting without Patch Correspondence for Accurate and
Dense Contrastive Representation Learning [79.43940012723539]
ADCLR is a self-supervised learning framework for learning accurate and dense vision representation.
Our approach achieves new state-of-the-art performance for contrastive methods.
arXiv Detail & Related papers (2023-06-23T07:38:09Z) - Federated Transfer-Ordered-Personalized Learning for Driver Monitoring
Application [15.731990691086123]
Federated learning (FL) has been successfully applied to various domains, including driver monitoring applications (DMAs) on the internet of vehicles (IoV)
This paper proposes a federated transfer-ordered-personalized learning (FedTOP) framework to address the above problems and test on two real-world datasets.
The results demonstrate that the proposed FedTOP can be used as a highly accurate, streamlined, privacy-preserving, cybersecurity-oriented, and personalized framework for DMA.
arXiv Detail & Related papers (2023-01-12T06:12:04Z) - G-DetKD: Towards General Distillation Framework for Object Detectors via
Contrastive and Semantic-guided Feature Imitation [49.421099172544196]
We propose a novel semantic-guided feature imitation technique, which automatically performs soft matching between feature pairs across all pyramid levels.
We also introduce contrastive distillation to effectively capture the information encoded in the relationship between different feature regions.
Our method consistently outperforms the existing detection KD techniques, and works when (1) components in the framework are used separately and in conjunction.
arXiv Detail & Related papers (2021-08-17T07:44:27Z) - Webly Supervised Fine-Grained Recognition: Benchmark Datasets and An
Approach [115.91099791629104]
We construct two new benchmark webly supervised fine-grained datasets, WebFG-496 and WebiNat-5089, respectively.
For WebiNat-5089, it contains 5089 sub-categories and more than 1.1 million web training images, which is the largest webly supervised fine-grained dataset ever.
As a minor contribution, we also propose a novel webly supervised method (termed Peer-learning'') for benchmarking these datasets.
arXiv Detail & Related papers (2021-08-05T06:28:32Z) - Dataset Distillation with Infinitely Wide Convolutional Networks [18.837952916998947]
We apply distributed kernel based meta-learning framework to achieve state-of-the-art results for dataset distillation.
We obtain over 64% test accuracy on CIFAR-10 image classification task, a dramatic improvement over the previous best test accuracy of 40%.
Our state-of-the-art results extend across many other settings for MNIST, Fashion-MNIST, CIFAR-10, CIFAR-100, and SVHN.
arXiv Detail & Related papers (2021-07-27T18:31:42Z) - Precision-Weighted Federated Learning [1.8160945635344528]
We propose a novel algorithm that takes into account the variance of the gradients when computing the weighted average of the parameters of models trained in a Federated Learning setting.
Our method was evaluated using standard image classification datasets with two different data partitioning strategies (IID/non-IID) to measure the performance and speed of our method in resource-constrained environments.
arXiv Detail & Related papers (2021-07-20T17:17:10Z) - Cross-Dataset Collaborative Learning for Semantic Segmentation [17.55660581677053]
We present a simple, flexible, and general method for semantic segmentation, termed Cross-Dataset Collaborative Learning (CDCL)
Given multiple labeled datasets, we aim to improve the generalization and discrimination of feature representations on each dataset.
We conduct extensive evaluations on four diverse datasets, i.e., Cityscapes, BDD100K, CamVid, and COCO Stuff, with single-dataset and cross-dataset settings.
arXiv Detail & Related papers (2021-03-21T09:59:47Z) - Inception Convolution with Efficient Dilation Search [121.41030859447487]
Dilation convolution is a critical mutant of standard convolution neural network to control effective receptive fields and handle large scale variance of objects.
We propose a new mutant of dilated convolution, namely inception (dilated) convolution where the convolutions have independent dilation among different axes, channels and layers.
We explore a practical method for fitting the complex inception convolution to the data, a simple while effective dilation search algorithm(EDO) based on statistical optimization is developed.
arXiv Detail & Related papers (2020-12-25T14:58:35Z) - Meta-Generating Deep Attentive Metric for Few-shot Classification [53.07108067253006]
We present a novel deep metric meta-generation method to generate a specific metric for a new few-shot learning task.
In this study, we structure the metric using a three-layer deep attentive network that is flexible enough to produce a discriminative metric for each task.
We gain surprisingly obvious performance improvement over state-of-the-art competitors, especially in the challenging cases.
arXiv Detail & Related papers (2020-12-03T02:07:43Z)
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.