DenseNet: Generating Multi-Level Neural Networks from End-to-End Instructional Videos – Object segmentation has been extensively used as a tool to train human-machine interfaces to recognize objects and identify them in video. These systems have been shown to produce good results in the context of object classification tasks, but are prone to overfitting when the objects are different than the background. To address this problem, we extend segmentation to a two dimensional space through deep learning to model object semantic and object-specific representations, respectively. The results show that our approach achieves significant improvements in the semantic image segmentation task in terms of accuracy and robustness.
We propose a novel deep recurrent network architecture to build more complex neural networks by training its entire model independently from a single training data. We propose two separate layers, which are jointly trained to learn features of the input and learn representations, together with separate layers to control the model’s internal state and information content. Our two layers are compared against other state-of-the-art methods including ResNet, ConvNet, and ResNet. The state-of-the-art results demonstrate that the proposed architecture produces state-of-the-art results in terms of learning performance on many datasets, but not on the least of them, while in terms of learning rate on the most challenging datasets.
Determining Pointwise Gradients for Linear-valued Functions with Spectral Penalties
Deep Convolutional Auto-Encoder: Learning Unsophisticated Image Generators from Noisy Labels
DenseNet: Generating Multi-Level Neural Networks from End-to-End Instructional Videos
Training a Neural Network for Detection of Road Traffic Flowchart
Guaranteed Constrained Recurrent Neural Networks for Action RecognitionWe propose a novel deep recurrent network architecture to build more complex neural networks by training its entire model independently from a single training data. We propose two separate layers, which are jointly trained to learn features of the input and learn representations, together with separate layers to control the model’s internal state and information content. Our two layers are compared against other state-of-the-art methods including ResNet, ConvNet, and ResNet. The state-of-the-art results demonstrate that the proposed architecture produces state-of-the-art results in terms of learning performance on many datasets, but not on the least of them, while in terms of learning rate on the most challenging datasets.