scholarly journals A Dual Attention Network with Semantic Embedding for Few-Shot Learning

2019 ◽  
Vol 33 ◽  
pp. 9079-9086 ◽  
Author(s):  
Shipeng Yan ◽  
Songyang Zhang ◽  
Xuming He
Keyword(s):  
Complex Model ◽  
Training Data ◽  
Future Research ◽  
Learning Approaches ◽  
Learning Loss ◽  
Attention Network ◽  
Shot Classification ◽  
Recent Success ◽  
Meta Learning ◽  
Visual Concepts

Despite recent success of deep neural networks, it remains challenging to efficiently learn new visual concepts from limited training data. To address this problem, a prevailing strategy is to build a meta-learner that learns prior knowledge on learning from a small set of annotated data. However, most of existing meta-learning approaches rely on a global representation of images and a meta-learner with complex model structures, which are sensitive to background clutter and difficult to interpret. We propose a novel meta-learning method for few-shot classification based on two simple attention mechanisms: one is a spatial attention to localize relevant object regions and the other is a task attention to select similar training data for label prediction. We implement our method via a dual-attention network and design a semantic-aware meta-learning loss to train the meta-learner network in an end-to-end manner. We validate our model on three few-shot image classification datasets with extensive ablative study, and our approach shows competitive performances over these datasets with fewer parameters. For facilitating the future research, code and data split are available: https://github.com/tonysy/STANet-PyTorch

scholarly journals Consistent MetaReg: Alleviating Intra-task Discrepancy for Better Meta-knowledge

2020 ◽  
Author(s):  
Pinzhuo Tian ◽  
Lei Qi ◽  
Shaokang Dong ◽  
Yinghuan Shi ◽  
Yang Gao
Keyword(s):  
Regularization Method ◽  
Data Distribution ◽  
Training Data ◽  
Learning Approaches ◽  
Limited Data ◽  
Learning Models ◽  
Shot Classification ◽  
Meta Learning ◽  
Gradient Based ◽  
Regret Bound

In the few-shot learning scenario, the data-distribution discrepancy between training data and test data in a task usually exists due to the limited data. However, most existing meta-learning approaches seldom consider this intra-task discrepancy in the meta-training phase which might deteriorate the performance. To overcome this limitation, we develop a new consistent meta-regularization method to reduce the intra-task data-distribution discrepancy. Moreover, the proposed meta-regularization method could be readily inserted into existing optimization-based meta-learning models to learn better meta-knowledge. Particularly, we provide the theoretical analysis to prove that using the proposed meta-regularization, the conventional gradient-based meta-learning method can reach the lower regret bound. The extensive experiments also demonstrate the effectiveness of our method, which indeed improves the performances of the state-of-the-art gradient-based meta-learning models in the few-shot classification task.

scholarly journals Few-shot Classification of Aerial Scene Images via Meta-learning

2020 ◽  
Author(s):  
Pei Zhang ◽  
YIng Li ◽  
Dong Wang ◽  
Yunpeng Bai
Keyword(s):  
Classification Problem ◽  
Training Data ◽  
Scene Classification ◽  
Shot Classification ◽  
Training Stage ◽  
Testing Stage ◽  
Meta Learning ◽  
Cosine Distance ◽  
Query Sample

CNN-based methods have dominated the field of aerial scene classification for the past few years. While achieving remarkable success, CNN-based methods suffer from excessive parameters and notoriously rely on large amounts of training data. In this work, we introduce few-shot learning to the aerial scene classification problem. Few-shot learning aims to learn a model on base-set that can quickly adapt to unseen categories in novel-set, using only a few labeled samples. To this end, we proposed a meta-learning method for few-shot classification of aerial scene images. First, we train a feature extractor on all base categories to learn a representation of inputs. Then in the meta-training stage, the classifier is optimized in the metric space by cosine distance with a learnable scale parameter. At last, in the meta-testing stage, the query sample in the unseen category is predicted by the adapted classifier given a few support samples. We conduct extensive experiments on two challenging datasets: NWPU-RESISC45 and RSD46-WHU. The experimental results show that our method outperforms three state-of-the-art few-shot algorithms and one typical CNN-based method, D-CNN. Furthermore, several ablation experiments are conducted to investigate the effects of dataset scale and support shots; the experiment results confirm that our model is specifically effective in few-shot settings.

scholarly journals Resource Management and Model Personalization for Federated Learning over Wireless Edge Networks

2021 ◽  
Vol 10 (1) ◽  
pp. 17
Author(s):  
Ravikumar Balakrishnan ◽  
Mustafa Akdeniz ◽  
Sagar Dhakal ◽  
Arjun Anand ◽  
Ariela Zeira ◽  
...  
Keyword(s):  
Resource Management ◽  
High Efficiency ◽  
Training Data ◽  
Convergence Time ◽  
Distributed Data ◽  
Learning Approaches ◽  
Training Performance ◽  
Meta Learning ◽  
Benchmark Datasets ◽  
Model Personalization

Client and Internet of Things devices are increasingly equipped with the ability to sense, process, and communicate data with high efficiency. This is resulting in a major shift in machine learning (ML) computation at the network edge. Distributed learning approaches such as federated learning that move ML training to end devices have emerged, promising lower latency and bandwidth costs and enhanced privacy of end users’ data. However, new challenges that arise from the heterogeneous nature of the devices’ communication rates, compute capabilities, and the limited observability of the training data at each device must be addressed. All these factors can significantly affect the training performance in terms of overall accuracy, model fairness, and convergence time. We present compute-communication and data importance-aware resource management schemes optimizing these metrics and evaluate the training performance on benchmark datasets. We also develop a federated meta-learning solution, based on task similarity, that serves as a sample efficient initialization for federated learning, as well as improves model personalization and generalization across non-IID (independent, identically distributed) data. We present experimental results on benchmark federated learning datasets to highlight the performance gains of the proposed methods in comparison to the well-known federated averaging algorithm and its variants.

scholarly journals RS-SSKD: Self-Supervision Equipped with Knowledge Distillation for Few-Shot Remote Sensing Scene Classification

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1566
Author(s):  
Pei Zhang ◽  
Ying Li ◽  
Dong Wang ◽  
Jiyue Wang
Keyword(s):  
Remote Sensing ◽  
State Of The Art ◽  
Ground Truth ◽  
Training Data ◽  
Scene Classification ◽  
Training Time ◽  
Shot Classification ◽  
Meta Learning ◽  
Knowledge Distillation ◽  
Few Data

While growing instruments generate more and more airborne or satellite images, the bottleneck in remote sensing (RS) scene classification has shifted from data limits toward a lack of ground truth samples. There are still many challenges when we are facing unknown environments, especially those with insufficient training data. Few-shot classification offers a different picture under the umbrella of meta-learning: digging rich knowledge from a few data are possible. In this work, we propose a method named RS-SSKD for few-shot RS scene classification from a perspective of generating powerful representation for the downstream meta-learner. Firstly, we propose a novel two-branch network that takes three pairs of original-transformed images as inputs and incorporates Class Activation Maps (CAMs) to drive the network mining, the most relevant category-specific region. This strategy ensures that the network generates discriminative embeddings. Secondly, we set a round of self-knowledge distillation to prevent overfitting and boost the performance. Our experiments show that the proposed method surpasses current state-of-the-art approaches on two challenging RS scene datasets: NWPU-RESISC45 and RSD46-WHU. Finally, we conduct various ablation experiments to investigate the effect of each component of the proposed method and analyze the training time of state-of-the-art methods and ours.

scholarly journals Multi-Stage Meta-Learning for Few-Shot with Lie Group Network Constraint

Entropy ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. 625
Author(s):  
Fang Dong ◽  
Li Liu ◽  
Fanzhang Li
Keyword(s):  
Gradient Descent ◽  
Common Knowledge ◽  
Training Data ◽  
Stiefel Manifold ◽  
Learning Approaches ◽  
Learning Models ◽  
Multi Stage ◽  
Meta Learning ◽  
Base Learner ◽  
Overfitting Problem

Deep learning has achieved many successes in different fields but can sometimes encounter an overfitting problem when there are insufficient amounts of labeled samples. In solving the problem of learning with limited training data, meta-learning is proposed to remember some common knowledge by leveraging a large number of similar few-shot tasks and learning how to adapt a base-learner to a new task for which only a few labeled samples are available. Current meta-learning approaches typically uses Shallow Neural Networks (SNNs) to avoid overfitting, thus wasting much information in adapting to a new task. Moreover, the Euclidean space-based gradient descent in existing meta-learning approaches always lead to an inaccurate update of meta-learners, which poses a challenge to meta-learning models in extracting features from samples and updating network parameters. In this paper, we propose a novel meta-learning model called Multi-Stage Meta-Learning (MSML) to post the bottleneck during the adapting process. The proposed method constrains a network to Stiefel manifold so that a meta-learner could perform a more stable gradient descent in limited steps so that the adapting process can be accelerated. An experiment on the mini-ImageNet demonstrates that the proposed method reached a better accuracy under 5-way 1-shot and 5-way 5-shot conditions.

scholarly journals Few-Shot Classification of Aerial Scene Images via Meta-Learning

2020 ◽  
Vol 13 (1) ◽  
pp. 108
Author(s):  
Pei Zhang ◽  
Yunpeng Bai ◽  
Dong Wang ◽  
Bendu Bai ◽  
Ying Li
Keyword(s):  
Classification Problem ◽  
Training Data ◽  
Scene Classification ◽  
Shot Classification ◽  
Training Stage ◽  
Testing Stage ◽  
Meta Learning ◽  
Cosine Distance ◽  
The Impact

Convolutional neural network (CNN) based methods have dominated the field of aerial scene classification for the past few years. While achieving remarkable success, CNN-based methods suffer from excessive parameters and notoriously rely on large amounts of training data. In this work, we introduce few-shot learning to the aerial scene classification problem. Few-shot learning aims to learn a model on base-set that can quickly adapt to unseen categories in novel-set, using only a few labeled samples. To this end, we proposed a meta-learning method for few-shot classification of aerial scene images. First, we train a feature extractor on all base categories to learn a representation of inputs. Then in the meta-training stage, the classifier is optimized in the metric space by cosine distance with a learnable scale parameter. At last, in the meta-testing stage, the query sample in the unseen category is predicted by the adapted classifier given a few support samples. We conduct extensive experiments on two challenging datasets: NWPU-RESISC45 and RSD46-WHU. The experimental results show that our method yields state-of-the-art performance. Furthermore, several ablation experiments are conducted to investigate the effects of dataset scale, the impact of different metrics and the number of support shots; the experiment results confirm that our model is specifically effective in few-shot settings.

scholarly journals Multilayer Soil Moisture Mapping at a Regional Scale from Multisource Data via a Machine Learning Method

2019 ◽  
Vol 11 (3) ◽  
pp. 284 ◽  
Author(s):  
Linglin Zeng ◽  
Shun Hu ◽  
Daxiang Xiang ◽  
Xiang Zhang ◽  
Deren Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Regional Scale ◽  
Remotely Sensed ◽  
Temporal Variations ◽  
Training Data ◽  
Estimation Accuracy ◽  
Learning Approaches ◽  
Remotely Sensed Data ◽  
Deep Soil

Soil moisture mapping at a regional scale is commonplace since these data are required in many applications, such as hydrological and agricultural analyses. The use of remotely sensed data for the estimation of deep soil moisture at a regional scale has received far less emphasis. The objective of this study was to map the 500-m, 8-day average and daily soil moisture at different soil depths in Oklahoma from remotely sensed and ground-measured data using the random forest (RF) method, which is one of the machine-learning approaches. In order to investigate the estimation accuracy of the RF method at both a spatial and a temporal scale, two independent soil moisture estimation experiments were conducted using data from 2010 to 2014: a year-to-year experiment (with a root mean square error (RMSE) ranging from 0.038 to 0.050 m3/m3) and a station-to-station experiment (with an RMSE ranging from 0.044 to 0.057 m3/m3). Then, the data requirements, importance factors, and spatial and temporal variations in estimation accuracy were discussed based on the results using the training data selected by iterated random sampling. The highly accurate estimations of both the surface and the deep soil moisture for the study area reveal the potential of RF methods when mapping soil moisture at a regional scale, especially when considering the high heterogeneity of land-cover types and topography in the study area.

scholarly journals SAR Target Recognition via Meta-Learning and Amortized Variational Inference

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5966
Author(s):  
Ke Wang ◽  
Gong Zhang
Keyword(s):  
Target Recognition ◽  
Probability Distributions ◽  
Automatic Target Recognition ◽  
Variational Inference ◽  
Training Data ◽  
Superior Performance ◽  
Small Data ◽  
Meta Learning ◽  
Radar Automatic Target Recognition ◽  
Global Parameters

The challenge of small data has emerged in synthetic aperture radar automatic target recognition (SAR-ATR) problems. Most SAR-ATR methods are data-driven and require a lot of training data that are expensive to collect. To address this challenge, we propose a recognition model that incorporates meta-learning and amortized variational inference (AVI). Specifically, the model consists of global parameters and task-specific parameters. The global parameters, trained by meta-learning, construct a common feature extractor shared between all recognition tasks. The task-specific parameters, modeled by probability distributions, can adapt to new tasks with a small amount of training data. To reduce the computation and storage cost, the task-specific parameters are inferred by AVI implemented with set-to-set functions. Extensive experiments were conducted on a real SAR dataset to evaluate the effectiveness of the model. The results of the proposed approach compared with those of the latest SAR-ATR methods show the superior performance of our model, especially on recognition tasks with limited data.

scholarly journals Meta Learning for Few-Shot One-Class Classification

AI ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 195-208
Author(s):  
Gabriel Dahia ◽  
Maurício Pamplona Segundo
Keyword(s):  
Feature Representation ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Target Class ◽  
Feature Representations ◽  
Shot Classification ◽  
Training Stage ◽  
Comparable Performance ◽  
Meta Learning ◽  
One Class Classification

We propose a method that can perform one-class classification given only a small number of examples from the target class and none from the others. We formulate the learning of meaningful features for one-class classification as a meta-learning problem in which the meta-training stage repeatedly simulates one-class classification, using the classification loss of the chosen algorithm to learn a feature representation. To learn these representations, we require only multiclass data from similar tasks. We show how the Support Vector Data Description method can be used with our method, and also propose a simpler variant based on Prototypical Networks that obtains comparable performance, indicating that learning feature representations directly from data may be more important than which one-class algorithm we choose. We validate our approach by adapting few-shot classification datasets to the few-shot one-class classification scenario, obtaining similar results to the state-of-the-art of traditional one-class classification, and that improves upon that of one-class classification baselines employed in the few-shot setting.

scholarly journals Social exclusion increases the executive function of attention networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Huoyin Zhang ◽  
Shiyunmeng Zhang ◽  
Jiachen Lu ◽  
Yi Lei ◽  
Hong Li
Keyword(s):  
Social Exclusion ◽  
Executive Control ◽  
Brain Regions ◽  
Negative Influence ◽  
Future Research ◽  
Research Attention ◽  
Attention Network ◽  
Attention Networks ◽  
The Social ◽  
And Control

AbstractPrevious studies in humans have shown that brain regions activating social exclusion overlap with those related to attention. However, in the context of social exclusion, how does behavioral monitoring affect individual behavior? In this study, we used the Cyberball game to induce the social exclusion effect in a group of participants. To explore the influence of social exclusion on the attention network, we administered the Attention Network Test (ANT) and compared results for the three subsystems of the attention network (orienting, alerting, and executive control) between exclusion (N = 60) and inclusion (N = 60) groups. Compared with the inclusion group, the exclusion group showed shorter overall response time and better executive control performance, but no significant differences in orienting or alerting. The excluded individuals showed a stronger ability to detect and control conflicts. It appears that social exclusion does not always exert a negative influence on individuals. In future research, attention to network can be used as indicators of social exclusion. This may further reveal how social exclusion affects individuals' psychosomatic mechanisms.

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