Fast Label Embedding for Discrete Product Product Pairing – Deep learning can be seen as a way to transform a neural network into a pre-trained neural network. However, deep learning can only handle small tasks and can be a more difficult task to tackle. In this paper, we propose a novel deep learning method, named Deep-NN, which can learn to create models which are a good candidate for training an end-to-end (ET) model. Our model is inspired from the traditional deep architecture and combines the architecture with the ability to perform non-linear feature extraction and semantic segmentation. In both cases, the models are a very efficient and robust way of learning to learn to build complex models. Through this, we learn a feature embedding which takes into account the data complexity, and also perform segmentation of the models. Experiments on the Flickr30K dataset demonstrate that the proposed approach outperforms the state-of-the-art deep learning methods on both MNIST and CalTech datasets.
Despite the recent success of learning structured classifiers, the main challenge is to find the right balance between classification performance and training data quality, which in turn requires large amounts of manual annotation. Many previous efforts to address the difficulty of labeling training examples in a single action, using machine learning, have focused on dealing with a single task. However, learning a complex feature vector of a data set can be time consuming, and to deal with it, feature vectors are often pre-trained to do the same task. In this work, we address these issues by leveraging deep semantic learning to extract more complex features from a dataset for classification tasks. In particular, we design a novel framework to extract semantic feature predictions with the goal of reducing the computational cost of feature extraction. We demonstrate how this approach can speed up the classification process by up to an order of magnitude.
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Fast Label Embedding for Discrete Product Product Pairing
Learning a Modular Deep Learning Model with Online CorrectionDespite the recent success of learning structured classifiers, the main challenge is to find the right balance between classification performance and training data quality, which in turn requires large amounts of manual annotation. Many previous efforts to address the difficulty of labeling training examples in a single action, using machine learning, have focused on dealing with a single task. However, learning a complex feature vector of a data set can be time consuming, and to deal with it, feature vectors are often pre-trained to do the same task. In this work, we address these issues by leveraging deep semantic learning to extract more complex features from a dataset for classification tasks. In particular, we design a novel framework to extract semantic feature predictions with the goal of reducing the computational cost of feature extraction. We demonstrate how this approach can speed up the classification process by up to an order of magnitude.