Add to ORKGThis thesis is an investigation of unsupervised learning for image classification. The state-of-the-art image classification method is Convolutional Neural Network (CNN), which is a purely supervised learning method. We argue that despite of the triumph of supervised learning, unsupervised learning is still important and compatible with supervised learning. For example, in the situation where some classes have no training data at all, so called zero-shot learning task, unsupervised learning can leverage supervised learning to classify the images of unseen classes. We proposed a new zero-shot learning method based on CNN and several unsupervised learning algorithms. Our method achieves the state-of-the-art results on the largest public avail...
Most CNN models rely on the large-scale annotated training data, and the performance turns to be lo...
Current methods for training convolutional neural networks depend on large amounts of labeled sample...
International audiencePre-training general-purpose visual features with convolutional neural network...
This master thesis tackles the problem of unsupervised learning of visual representations with deep ...
Image Classification is a branch of computer vision where images are classified into categories. Thi...
For computer vision based appraoches such as image classification (Krizhevsky et al. 2012), object d...
Over the past few years, with the introduction of deep learning techniques such as convolution neura...
The power of deep neural networks comes mainly from huge labeled datasets. Even though it shines on ...
311 p. : il.[EN]This Thesis covers a broad period of research activities with a commonthread: learni...
While deep learning strategies achieve outstanding results in computer vision tasks, one issue remai...
Most recent unsupervised learning methods explore alternative objectives, often referred to as self-...
Research on image classification has grown rapidly in the field of machine learning. Many methods ha...
International audiencePart-based image classification aims at representing categories by small sets ...
The concept of Convolution Neural Network (ConvNet or CNN) is evaluated from the animal visual corte...
Convolutional neural networks (CNNs) have achieved unprecedented success in a variety of computer vi...
Most CNN models rely on the large-scale annotated training data, and the performance turns to be lo...
Current methods for training convolutional neural networks depend on large amounts of labeled sample...
International audiencePre-training general-purpose visual features with convolutional neural network...
This master thesis tackles the problem of unsupervised learning of visual representations with deep ...
Image Classification is a branch of computer vision where images are classified into categories. Thi...
For computer vision based appraoches such as image classification (Krizhevsky et al. 2012), object d...
Over the past few years, with the introduction of deep learning techniques such as convolution neura...
The power of deep neural networks comes mainly from huge labeled datasets. Even though it shines on ...
311 p. : il.[EN]This Thesis covers a broad period of research activities with a commonthread: learni...
While deep learning strategies achieve outstanding results in computer vision tasks, one issue remai...
Most recent unsupervised learning methods explore alternative objectives, often referred to as self-...
Research on image classification has grown rapidly in the field of machine learning. Many methods ha...
International audiencePart-based image classification aims at representing categories by small sets ...
The concept of Convolution Neural Network (ConvNet or CNN) is evaluated from the animal visual corte...
Convolutional neural networks (CNNs) have achieved unprecedented success in a variety of computer vi...
Most CNN models rely on the large-scale annotated training data, and the performance turns to be lo...
Current methods for training convolutional neural networks depend on large amounts of labeled sample...
International audiencePre-training general-purpose visual features with convolutional neural network...