Tensor-based approaches for learning flexible neural networks

To evaluate how our transfer method performs on this very different target task, we use a network pre-trained on 1512 ImageNet object classes and apply our transfer methodology to

The target output in visual recognition ranges from image-level labels (object/image classification) [23], lo- cation and extent of objects in the form of bounding boxes

We construct multi-modal concept repre- sentations by concatenating a skip-gram linguistic representation vector with a vi- sual concept representation vector com- puted using

 pushing forward to weakly supervised learning the problem Medical Image Segmentation of Thoracic Organs at Risk in CT-images..

Figure 6: Accuracy stability on normal dataset (for class ”mountain bike”), showing a gradual increase of accuracy with respect to decreasing neurons’ criticality in case of

Artificial intelligence has occupied the place of the sphere of research and today includes the paradigm of learning – machine learning [12; 13], which differs from the

The C- DSSM contains a word hashing layer that transforms each word into a letter-tri-gram input representation, a convolutional layer to extract local contextual features,

– Convolution layers & Pooling layers + global architecture – Training algorithm + Dropout Regularization.. • Useful