scholarly journals Image Classification using ImageNet Classifiers in Environments with Limited Data

2021 ◽  
Author(s):  
Anirvin Sharma ◽  
Abhinav Singh ◽  
Tanupriya Choudhury ◽  
Tanmay Sarkar
Keyword(s):  
Image Classification ◽  
Transfer Learning ◽  
Data Augmentation ◽  
Classification Algorithms ◽  
Limited Data ◽  
Training Time ◽  
Learning Framework ◽  
Rare Phenomena ◽  
Fully Connected ◽  
Compare And Contrast

Abstract In this research, we compare and contrast various image classification algorithms and how effective they are in specific problem sets where data might be scarce such as prediction of rare phenomena (for example, natural calamities), enterprise solutions etc. We have employed various state-of-the-art algorithms in this study credited to have been some of the best classifiers at the time of their inception. These classifiers have also been suspected to fall prey to overfitting on the datasets they were initially tested on viz. ImageNet and Common Objects in Context (COCO); we test to what extent these classifiers tend to generalize to the new data provided by us in a transfer learning framework. We utilize transfer learning on the ImageNet classifiers to adapt to our smaller dataset and examine various techniques such as data augmentation, batch normalization, dropout etc. to mitigate overfitting. All the classifiers follow a standard fully connected architecture. The end result should provide the reader with an overall analysis of which algorithm or approach to use in conditions where data might be limited while also giving a brief overview of the progress of image classification algorithms since their advent. We also provide an analysis on the effectiveness of data augmentation in limited datasets by providing results achieved with and without utilizing data augmentation. In our case, we found the MobileNet (with its lightweight nature contributing to low computational costs) and InceptionV3 (owing to its lower training time) to be the best performing classifiers for applying transfer learning to limited datasets out of the classifiers we have used for our study. This paper aims to establish preemptive standards that can be used to evaluate the models which can be used in object recognition, and image classification for problems containing limited amounts of data.

scholarly journals Transfer learning for hostel image classification

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Chanattra Ammatmanee ◽  
Lu Gan
Keyword(s):  
Image Classification ◽  
Transfer Learning ◽  
Data Augmentation ◽  
Image Database ◽  
Learning Ability ◽  
Learning Technology ◽  
Human Errors ◽  
Content Type ◽  
Training Time ◽  
Processing Steps

PurposeBecause of the fast-growing digital image collections on online platforms and the transfer learning ability of deep learning technology, image classification could be improved and implemented for the hostel domain, which has complex clusters of image contents. This paper aims to test the potential of 11 pretrained convolutional neural network (CNN) with transfer learning for hostel image classification on the first hostel image database to advance the knowledge and fill the gap academically, as well as to suggest an alternative solution in optimal image classification with less labour cost and human errors to those who manage hostel image collections.Design/methodology/approachThe hostel image database is first created with data pre-processing steps, data selection and data augmentation. Then, the systematic and comprehensive investigation is divided into seven experiments to test 11 pretrained CNNs which transfer learning was applied and parameters were fine-tuned to match this newly created hostel image dataset. All experiments were conducted in Google Colaboratory environment using PyTorch.FindingsThe 7,350 hostel image database is created and labelled into seven classes. Furthermore, its experiment results highlight that DenseNet 121 and DenseNet 201 have the greatest potential for hostel image classification as they outperform other CNNs in terms of accuracy and training time.Originality/valueThe fact that there is no existing academic work dedicating to test pretrained CNNs with transfer learning for hostel image classification and no existing hostel image-only database have made this paper a novel contribution.

scholarly journals Voiceprint Identification for Limited Dataset Using the Deep Migration Hybrid Model Based on Transfer Learning

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2399 ◽  
Author(s):  
Cunwei Sun ◽  
Yuxin Yang ◽  
Chang Wen ◽  
Kai Xie ◽  
Fangqing Wen
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Hybrid Model ◽  
Data Augmentation ◽  
Small Sample ◽  
Restricted Boltzmann Machine ◽  
Small Samples ◽  
Boltzmann Machine ◽  
Training Time

The convolutional neural network (CNN) has made great strides in the area of voiceprint recognition; but it needs a huge number of data samples to train a deep neural network. In practice, it is too difficult to get a large number of training samples, and it cannot achieve a better convergence state due to the limited dataset. In order to solve this question, a new method using a deep migration hybrid model is put forward, which makes it easier to realize voiceprint recognition for small samples. Firstly, it uses Transfer Learning to transfer the trained network from the big sample voiceprint dataset to our limited voiceprint dataset for the further training. Fully-connected layers of a pre-training model are replaced by restricted Boltzmann machine layers. Secondly, the approach of Data Augmentation is adopted to increase the number of voiceprint datasets. Finally, we introduce fast batch normalization algorithms to improve the speed of the network convergence and shorten the training time. Our new voiceprint recognition approach uses the TLCNN-RBM (convolutional neural network mixed restricted Boltzmann machine based on transfer learning) model, which is the deep migration hybrid model that is used to achieve an average accuracy of over 97%, which is higher than that when using either CNN or the TL-CNN network (convolutional neural network based on transfer learning). Thus, an effective method for a small sample of voiceprint recognition has been provided.

Data Augmentation and Transfer Learning Applied to Charcoal Image Classification

2019 ◽  
Author(s):  
Luciana T. Menon ◽  
Israel A. Laurensi ◽  
Manoel C. Penna ◽  
Luiz E. S. Oliveira ◽  
Alceu S. Britto
Keyword(s):  
Image Classification ◽  
Transfer Learning ◽  
Data Augmentation

A Semi-Supervised Active Learning Framework for Image Classification

2014 ◽  
Vol 556-562 ◽  
pp. 4765-4769
Author(s):  
Han Yi Li ◽  
Ming Yang ◽  
Nan Nan Kang ◽  
Lu Lu Yue
Keyword(s):  
Active Learning ◽  
Image Classification ◽  
Supervised Learning ◽  
The Other ◽  
Training Data ◽  
Classification Error ◽  
Classification Algorithms ◽  
Learning Framework ◽  
User Query ◽  
Changing Rate

In this paper, a novel image classification method, incorporating active learning and semi-supervised learning (SSL), is proposed. In this method, two classifiers are needed where one is trained by labeled data and some unlabeled data, while the other one is trained only by labeled data. Specifically, in each round, two classifiers iterate to select useful examples in contention for user query. Then we compute the label changing rate for every unlabeled example in each classifier. Those examples in which the label changing rate is zero and the label in the two classifiers is the same are selected to add into the training data of the first classifier. Our experimental results show that our method significantly reduced the need of labeled examples, while at the same time reducing classification error compared with widely used image classification algorithms.

scholarly journals A Transfer Learning Evaluation of Deep Neural Networks for Image Classification

2022 ◽  
Vol 4 (1) ◽  
pp. 22-41
Author(s):  
Nermeen Abou Baker ◽  
Nico Zengeler ◽  
Uwe Handmann
Keyword(s):  
Image Classification ◽  
Transfer Learning ◽  
High Performance ◽  
Target Domain ◽  
Training Time ◽  
Learning Evaluation ◽  
Learning Technique ◽  
General Network ◽  
Model Size ◽  
Classification Tasks

Transfer learning is a machine learning technique that uses previously acquired knowledge from a source domain to enhance learning in a target domain by reusing learned weights. This technique is ubiquitous because of its great advantages in achieving high performance while saving training time, memory, and effort in network design. In this paper, we investigate how to select the best pre-trained model that meets the target domain requirements for image classification tasks. In our study, we refined the output layers and general network parameters to apply the knowledge of eleven image processing models, pre-trained on ImageNet, to five different target domain datasets. We measured the accuracy, accuracy density, training time, and model size to evaluate the pre-trained models both in training sessions in one episode and with ten episodes.

scholarly journals Filtered BERT: Similarity Filter-Based Augmentation with Bidirectional Transfer Learning for Protected Health Information Prediction in Clinical Documents

2021 ◽  
Vol 11 (8) ◽  
pp. 3668
Author(s):  
Min Kang ◽  
Kye Hwa Lee ◽  
Youngho Lee
Keyword(s):  
Health Information ◽  
Transfer Learning ◽  
Data Augmentation ◽  
Protected Health Information ◽  
Limited Data ◽  
Secondary Use ◽  
Data Environment

For the secondary use of clinical documents, it is necessary to de-identify protected health information (PHI) in documents. However, the difficulty lies in the fact that there are few publicly annotated PHI documents. To solve this problem, in this study, we propose a filtered bidirectional encoder representation from transformers (BERT)-based method that predicts a masked word and validates the word again through a similarity filter to construct augmented sentences. The proposed method effectively performs data augmentation. The results show that the augmentation method based on filtered BERT improved the performance of the model. This suggests that our method can effectively improve the performance of the model in the limited data environment.

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