Few-Shot Learning With Geometric Constraints

Jung, Hong-Gyu; Lee, Seong–Whan

doi:10.1109/tnnls.2019.2957187

Cited by 54 publications

(20 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[24] additionally learns a category-agnostic mapping to transform the mean-sample representation to its class-prototype representation, considering the prototypes in Prototypical Networks [13] may be inaccurate. Furthermore, finetuning with geometric constraints is applied in [25] to force intra-class similarity and inter-class disparity. [26] transfers knowledge from base classes to novel classes by embedding image features as class adapting principal directions, instead of the vector representation adopted by previous works.…”

Section: Related Work a Few-shot Learningmentioning

confidence: 99%

GenDet: Meta Learning to Generate Detectors From Few Shots

Liu

Wang

Kuang

et al. 2022

IEEE Trans. Neural Netw. Learning Syst.

View full text Add to dashboard Cite

Object detection has made enormous progress and has been widely used in many applications. However, it performs poorly when only limited training data is available for novel classes which the model has never seen before. Most existing approaches solve few-shot detection tasks implicitly without directly modeling the detectors for novel classes. In this paper, we propose GenDet, a new meta-learning based framework which can effectively generate object detectors for novel classes from few shots, and thus conducts few-shot detection tasks explicitly. The detector generator is trained by numerous few-shot detection tasks sampled from base classes each with sufficient samples, and thus it is expected to generalize well on novel classes. An adaptive pooling module is further introduced to suppress distracting samples and aggregate the detectors generated from multiple shots. Moreover, we propose to train a reference detector for each base class in the conventional way, with which to guide the training of the detector generator. The reference detectors and the detector generator can be trained simultaneously. Finally, the generated detectors of different classes are encouraged to be orthogonal to each other for better generalization. The proposed approach is extensively evaluated on the ImageNet, VOC and COCO datasets under various few-shot detection settings, and it achieves new state-of-the-art results.

show abstract

Section: Related Work a Few-shot Learningmentioning

confidence: 99%

GenDet: Meta Learning to Generate Detectors From Few Shots

Liu

Wang

Kuang

et al. 2022

IEEE Trans. Neural Netw. Learning Syst.

View full text Add to dashboard Cite

show abstract

“…Probably because physicians can use the experience from themselves to recognize them, 22,23 and the network cannot. Moreover, is not this one of the mechanisms of meta‐learning? 24 We may use this mechanism to address the issue of limited deep‐learning‐based models with the lack of training samples. So, why don't we use the principle of meta‐learning to build a network?…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, is not this one of the mechanisms of meta-learning? 24 We may use this mechanism to address the issue of limited deep-learning-based models with the lack of training samples. So, why don't we use the principle of meta-learning to build a network?…”

Section: Introductionmentioning

confidence: 99%

Learning to learn by yourself: Unsupervised meta‐learning with self‐knowledge distillation for COVID‐19 diagnosis from pneumonia cases

et al. 2021

View full text Add to dashboard Cite

The goal of diagnosing the coronavirus disease 2019 (COVID‐19) from suspected pneumonia cases, that is, recognizing COVID‐19 from chest X‐ray or computed tomography (CT) images, is to improve diagnostic accuracy, leading to faster intervention. The most important and challenging problem here is to design an effective and robust diagnosis model. To this end, there are three challenges to overcome: (1) The lack of training samples limits the success of existing deep‐learning‐based methods. (2) Many public COVID‐19 data sets contain only a few images without fine‐grained labels. (3) Due to the explosive growth of suspected cases, it is urgent and important to diagnose not only COVID‐19 cases but also the cases of other types of pneumonia that are similar to the symptoms of COVID‐19. To address these issues, we propose a novel framework called Unsupervised Meta‐Learning with Self‐Knowledge Distillation to address the problem of differentiating COVID‐19 from pneumonia cases. During training, our model cannot use any true labels and aims to gain the ability of learning to learn by itself. In particular, we first present a deep diagnosis model based on a relation network to capture and memorize the relation among different images. Second, to enhance the performance of our model, we design a self‐knowledge distillation mechanism that distills knowledge within our model itself. Our network is divided into several parts, and the knowledge in the deeper parts is squeezed into the shallow ones. The final results are derived from our model by learning to compare the features of images. Experimental results demonstrate that our approach achieves significantly higher performance than other state‐of‐the‐art methods. Moreover, we construct a new COVID‐19 pneumonia data set based on text mining, consisting of 2696 COVID‐19 images (347 X‐ray + 2349 CT), 10,155 images (9661 X‐ray + 494 CT) about other types of pneumonia, and the fine‐grained labels of all. Our data set considers not only a bacterial infection or viral infection which causes pneumonia but also a viral infection derived from the influenza virus or coronavirus.

show abstract

“…Few-shot learning aims to solve the data scarcity issue, which can recognize novel categories effectively with only a handful of labeled samples by leveraging the prior knowledge learned from previous categories. Most few-shot learning studies concentrate on computer vision domain (Fei-Fei et al, 2006;Finn et al, 2017;Jung and Lee, 2020). Recently, to handle various new or unacquainted intents popped up quickly from different domains, some few-shot IC/SF models are proposed (Geng et al, 2020;Hou et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

An Explicit-Joint and Supervised-Contrastive Learning Framework for Few-Shot Intent Classification and Slot Filling

Liu¹,

Zhang²,

Zhang³

et al. 2021

Findings of the Association for Computational Linguistics: EMNLP 2021

View full text Add to dashboard Cite

Intent classification (IC) and slot filling (SF) are critical building blocks in task-oriented dialogue systems. These two tasks are closelyrelated and can flourish each other. Since only a few utterances can be utilized for identifying fast-emerging new intents and slots, data scarcity issue often occurs when implementing IC and SF. However, few IC/SF models perform well when the number of training samples per class is quite small. In this paper, we propose a novel explicit-joint and supervisedcontrastive learning framework for few-shot intent classification and slot filling. Its highlights are as follows. (i) The model extracts intent and slot representations via bidirectional interactions, and extends prototypical network to achieve explicit-joint learning, which guarantees that IC and SF tasks can mutually reinforce each other. (ii) The model integrates with supervised contrastive learning, which ensures that samples from same class are pulled together and samples from different classes are pushed apart. In addition, the model follows a not common but practical way to construct the episode, which gets rid of the traditional setting with fixed way and shot, and allows for unbalanced datasets. Extensive experiments on three public datasets show that our model can achieve promising performance.

show abstract

Few-Shot Learning With Geometric Constraints

Cited by 54 publications

References 27 publications

GenDet: Meta Learning to Generate Detectors From Few Shots

GenDet: Meta Learning to Generate Detectors From Few Shots

Learning to learn by yourself: Unsupervised meta‐learning with self‐knowledge distillation for COVID‐19 diagnosis from pneumonia cases

An Explicit-Joint and Supervised-Contrastive Learning Framework for Few-Shot Intent Classification and Slot Filling

Contact Info

Product

Resources

About