Add to ORKGImage classification has been used in many real-world applications such as self-driving cars, recommender systems and recognition systems. With the advent of deep learning and artificial intelligence, the accuracy of such systems has increased greatly. However, such methods require large amounts of labelled data during the training phase in order to achieve a high level of accuracy. This data may not be available in all cases. Furthermore, domain shifts which occur in many real-world situations may also affect the accuracy of the classifier. This project studies the use of transfer learning, in particular, domain adaptation on Convolutional Neural Networks (CNNs) to improve classifier accuracy in cases of domain shifts where labelled data i...
Deep neural networks can learn powerful representations from massive amounts of labeled data; howeve...
Images seen during test time are often not from the same distribution as images used for learning. T...
The advent of deep convolutional networks has powered a new wave of progress in visual recognition. ...
Image classification has been used in many real-world applications such as self-driving cars, recomm...
Deep learning method, convolutional neural network (CNN) outperforms conventional machine learning m...
Can a gradual transition from the source to the target dataset improve knowledge transfer when fine-...
Can a gradual transition from the source to the target dataset improve knowledge transfer when fine-...
Parameter fine tuning is a transfer learning approach whereby learned parameters from pre-trained so...
Can a gradual transition from the source to the target dataset improve knowledge transfer when fine-...
The number of application areas of deep neural networks for image classification is continuously gro...
The number of application areas of deep neural networks for image classification is continuously gro...
Recent studies reveal that a deep neural network can learn transferable features which generalize we...
A basic assumption of statistical learning theory is that train and test data are drawn from the sam...
Parameter fine tuning is a transfer learning approach whereby learned parameters from pre-trained so...
Images seen during test time are often not from the same distribution as images used for learning. T...
Deep neural networks can learn powerful representations from massive amounts of labeled data; howeve...
Images seen during test time are often not from the same distribution as images used for learning. T...
The advent of deep convolutional networks has powered a new wave of progress in visual recognition. ...
Image classification has been used in many real-world applications such as self-driving cars, recomm...
Deep learning method, convolutional neural network (CNN) outperforms conventional machine learning m...
Can a gradual transition from the source to the target dataset improve knowledge transfer when fine-...
Can a gradual transition from the source to the target dataset improve knowledge transfer when fine-...
Parameter fine tuning is a transfer learning approach whereby learned parameters from pre-trained so...
Can a gradual transition from the source to the target dataset improve knowledge transfer when fine-...
The number of application areas of deep neural networks for image classification is continuously gro...
The number of application areas of deep neural networks for image classification is continuously gro...
Recent studies reveal that a deep neural network can learn transferable features which generalize we...
A basic assumption of statistical learning theory is that train and test data are drawn from the sam...
Parameter fine tuning is a transfer learning approach whereby learned parameters from pre-trained so...
Images seen during test time are often not from the same distribution as images used for learning. T...
Deep neural networks can learn powerful representations from massive amounts of labeled data; howeve...
Images seen during test time are often not from the same distribution as images used for learning. T...
The advent of deep convolutional networks has powered a new wave of progress in visual recognition. ...