Hierarchical few-shot learning based on coarse- and fine-grained relation network

Wu, Zhiping; Zhao, Hong

doi:10.1007/s10462-022-10223-3

Cited by 7 publications

(3 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To evaluate the method in this paper, the datasets were used for the experiments: MNIST [8], FashionMNIST [9], CIFAR10 [10], Cats vs Dogs [11], and miniImagenet [12]. MNIST is a 0-9 digital image dataset consisting of 60,000 training samples and 10,000 test samples, each of which is a handwritten 28×28 grayscale digital image.…”

Section: Datasetsmentioning

confidence: 99%

“…Moreover, the parameter R can be adjusted according to different models to achieve the best regularization effect from the training process. To evaluate this approach, we conducted tests comparing different CNNs on MNIST [8], FashionMNIST [9], CIFAR-10 [10], Cats vs Dogs [11], and miniImagenet [12] datasets, and used accuracy, F1, recall, and precision metrics data to evaluate the results. The experimental results show that the method can improve the model performance of commonly used Light CNNs and currently popular Transfer CNNs (InceptionResNet [13], VGG19 [14], ResNet50 [15], and InceptionV3 [16]).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improved generalization performance of convolutional neural networks with LossDA

Liu

Zhao

2022

Appl Intell

View full text Add to dashboard Cite

In recent years, convolutional neural networks (CNNs) have been used in many fields. Nowadays, CNNs have a high learning capability, and this learning capability is accompanied by a more complex model architecture. Complex model architectures allow CNNs to learn more data features, but such a learning process tends to reduce the training model’s ability to generalize to unknown data, and may be associated with problems of overfitting. Although many regularization methods have been proposed, such as data augmentation, batch normalization, and Dropout, research on improving generalization performance is still a common concern in the training process of robust CNNs. In this paper, we propose a dynamically controllable adjustment method, which we call LossDA, that embeds a disturbance variable in the fully-connected layer. The trend of this variable is kept consistent with the training loss, while the magnitude of the variable can be preset to adapt to the training process of different models. Through this dynamic adjustment, the training process of CNNs can be adaptively adjusted. The whole regularization process can improve the generalization performance of CNNs while helping to suppress overfitting. To evaluate this method, this paper conducts comparative experiments on MNIST, FashionMNIST, CIFAR-10, Cats_vs_Dogs, and miniImagenet datasets. The experimental results show that the method can improve the model performance of Light CNNs and Transfer CNNs (InceptionResNet, VGG19, ResNet50, and InceptionV3). The average maximum improvement in accuracy of Light CNNs is 4.62%, F1 is 3.99%, and Recall is 4.69%. The average maximum improvement accuracy of Transfer CNNs is 4.17%, F1 is 5.64%, and Recall is 4.05%.

show abstract

Section: Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improved generalization performance of convolutional neural networks with LossDA

Liu

Zhao

2022

Appl Intell

View full text Add to dashboard Cite

show abstract

“…For instance, Sung Whan Yoon et al [10] proposed the Neural Network Augmented with a Task-Adaptive Projection method, which utilizes a scenario-based meta-learning strategy and linear projection for image classification. Zhiping Wu et al [11] extracted features from different layers based on coarse/fine-grained relation networks, where coarse-grained classification was applied to shallow features followed by fine-grained classification on deep features.…”

Section: Introductionmentioning

confidence: 99%

Local Contrast Learning for One-Shot Learning

Zhang,

Yuan,

Luo

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

Learning a deep model from small data is an opening and challenging problem. In high-dimensional spaces, few samples only occupy an extremely small portion of the space, often exhibiting sparsity issues. Classifying in this globally sparse sample space poses significant challenges. However, by using a single sample category as a reference object for comparing and recognizing other samples, it is possible to construct a local space. Conducting contrastive learning in this local space can overcome the sparsity issue of a few samples. Based on this insight, we proposed a novel deep learning approach named Local Contrast Learning (LCL). This is analogous to a key insight into human cognitive behavior, where humans identify the objects in a specific context by contrasting them with the objects in that context or from their memory. LCL is used to train a deep model that can contrast the recognized sample with a couple of contrastive samples that are randomly drawn and shuffled. On a one-shot classification task on Omniglot, the deep model-based LCL with 86 layers and 1.94 million parameters, which was trained on a tiny dataset with only 60 classes and 20 samples per class, achieved an accuracy of 98.95%. Furthermore, it achieved an accuracy of 99.24% at 156 classes and 20 samples per class. LCL is a fundamental idea that can be applied to alleviate the parametric model’s overfitting resulting from a lack of training samples.

show abstract

Multitask-Based Cluster Transmission for Few-Shot Text Classification

Dong

Jiang

et al. 2023

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Hierarchical few-shot learning based on coarse- and fine-grained relation network

Cited by 7 publications

References 43 publications

Improved generalization performance of convolutional neural networks with LossDA

Improved generalization performance of convolutional neural networks with LossDA

Local Contrast Learning for One-Shot Learning

Multitask-Based Cluster Transmission for Few-Shot Text Classification

Contact Info

Product

Resources

About