Improving the generalization performance of deep networks by dual pattern learning with adversarial adaptation

Zhang, Haimin; Xu, Min

doi:10.1016/j.knosys.2020.106016

Cited by 8 publications

(5 citation statements)

References 36 publications

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“…Our system only needs to update the distribution of labels in corresponding environment, which is easier than the re-training or transferring of models. We performed our system and existing methods on the samples of CIFAR-10 [12][13][14], CIFAR-100 [15][16][17] and Mini-ImageNet [18][19][20]. All of these evaluations proved the effectiveness of our system.…”

Section: Figure 1: How the Distribution Of Labels Increases The Accur...mentioning

confidence: 82%

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Environment Adaptive Deep Learning Classification System Based on One-shot Guidance

Jin¹,

Pei²,

Zhao³

et al. 2022

Computers, Materials &Amp; Continua

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When utilizing the deep learning models in some real applications, the distribution of the labels in the environment can be used to increase the accuracy. Generally, to compute this distribution, there should be the validation set that is labeled by the ground truths. On the other side, the dependency of ground truths limits the utilization of the distribution in various environments. In this paper, we carried out a novel system for the deep learning-based classification to solve this problem. Firstly, our system only uses one validation set with ground truths to compute some hyper parameters, which is named as one-shot guidance. Secondly, in an environment, our system builds the validation set and labels this by the prediction results, which does not need any guidance by the ground truths. Thirdly, the computed distribution of labels by the validation set selectively cooperates with the probability of labels by the output of models, which is to increase the accuracy of predict results on testing samples. We selected six popular deep learning models on three real datasets for the evaluation. The experimental results show that our system can achieve higher accuracy than state-of-art methods while reducing the dependency of labeled validation set.

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Section: Figure 1: How the Distribution Of Labels Increases The Accur...mentioning

confidence: 82%

“…CIFAR-10 has 60000 samples that belong to 10 labels [12][13][14]. We separated this dataset to 50000 training samples and 10000 testing samples.…”

Section: The Evaluation On Cifar-10mentioning

confidence: 99%

Environment Adaptive Deep Learning Classification System Based on One-shot Guidance

Jin¹,

Pei²,

Zhao³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…We performed our framework and the existing methods on the samples of CIFAR-10 [15][16][17], CIFAR-100 [18][19][20], and Mini-ImageNet [21][22][23]. All of these evaluations proved the effectiveness of our framework.…”

Section: Introductionmentioning

confidence: 78%

“…is dataset is just like the CIFAR-10, except it has 100 classes containing 600 images each [18][19][20]. ere are 500 training images and 100 testing images per label.…”

Section: E Evaluation On Cifar-100mentioning

confidence: 99%

Improving the Performance of Deep Learning Model‐Based Classification by the Analysis of Local Probability

Jin

Jiao

et al. 2021

Complexity

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Generally, the performance of deep learning-based classification models is highly related to the captured features of training samples. When a sample is not clear or contains a similar number of features of many objects, we cannot easily classify what it is. Actually, human beings classify objects by not only the features but also some information such as the probability of these objects in an environment. For example, when we know further information such as one object has a higher probability in the environment than the others, we can easily give the answer about what is in the sample. We call this kind of probability as local probability as this is related to the local environment. In this paper, we carried out a new framework that is named L-PDL to improve the performance of deep learning based on the analysis of this kind of local probability. Firstly, our method trains the deep learning model on the training set. Then, we can get the probability of objects on each sample by this trained model. Secondly, we get the posterior local probability of objects on the validation set. Finally, this probability conditionally cooperates with the probability of objects on testing samples. We select three popular deep learning models on three real datasets for the evaluation. The experimental results show that our method can obviously improve the performance on the real datasets, which is better than the state-of-the-art methods.

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“…CIFAR-10 contains 50000 training samples and 10000 testing ones, which can be summarized as 10 labels and all of the samples belong RGB image [11][12][13]. 50000 training samples are to train the deep learning models.…”

Section: The Evaluation On Cifar-10mentioning

confidence: 99%

Solving the Feature Diversity Problem Based on Multi-Model Scheme

Jin¹,

Zhao²,

Pei³

et al. 2021

Journal on Artificial Intelligence

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Generally, the performance of deep learning models is related to the captured features of training samples. When the training samples belong to different domains, the diverse features may increase the difficulty of training high performance models. In this paper, we built a new framework that generates multiple models on the organized samples to increase the accuracy of classification. Firstly, our framework selects some existing models and trains each of them on organized training sets to get multiple trained models. Secondly, we select some of them based on a validation set. Finally, we use some fusion method on the outputs of the selected models to get more accurate results. The experimental results show that our framework achieved higher accuracy than the existing methods. Our framework can be an option for the deep learning system to increase the classification accuracy.

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Improving the generalization performance of deep networks by dual pattern learning with adversarial adaptation

Cited by 8 publications

References 36 publications

Environment Adaptive Deep Learning Classification System Based on One-shot Guidance

Environment Adaptive Deep Learning Classification System Based on One-shot Guidance

Improving the Performance of Deep Learning Model‐Based Classification by the Analysis of Local Probability

Solving the Feature Diversity Problem Based on Multi-Model Scheme

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