Learning visual representations with neural networks for video captioning and image generation
Texte intégral
Figure
Documents relatifs
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
In Table 1, we report our single-image autoencoder-based results on this dataset along with those of the following state- of-the-art single image-based methods: KDE regression from
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
In our experiments, this version worked better in the Unsupervised and Transfer Learning Challenge, characterized by a non-discriminant linear classifier and very few labeled
We apply the random neural networks (RNN) [15], [16] developed for deep learning recently [17]–[19] to detecting network attacks using network metrics extract- ed from the
Deep learning architectures which have been recently proposed for the pre- diction of salient areas in images differ essentially by the quantity of convolution and pooling layers,
Model-free algorithms (lower branch), bypass the model-learning step and learn the value function directly from the interactions, primarily using a family of algorithms called
• a novel training process is introduced, based on a single image (acquired at a reference pose), which includes the fast creation of a dataset using a simulator allowing for