The Power of Adversarial Examples for Learning Deep Models


The Power of Adversarial Examples for Learning Deep Models – With the recent success of deep neural networks and deep reinforcement learning, a great deal of attention has been given to the task of learning models that are invariant to some external input that is similar to the user’s behavior. However, this problem is still subject to a number of issues. One of them is that, as the number of input variables increases, the model is unable to predict or predict the outcome. This is not a good situation if the model is not robust to the environment. This work aims to tackle this problem by making the models that are invariant to this input-dependent model. We propose an adaptive learning algorithm that learns models that are invariant with the input. Our algorithm leverages the fact that the model learned by the adaptive learning algorithm is a neural network, and that these models have a common structure that allows the robustness of the model. Our algorithm is not only robust, but it also provides feedback to the model to guide the learning process, which ensures that model is invariant to the input and the behavior of the user.

The problem of performing temporal matching is one of high importance in many applications such as visual search, face recognition and image processing. Due to the low temporal precision of the data, it is hard to compare features. We present a new neural network architecture, which uses a Convolutional Neural Network (CNN) for retrieval of face images as a basis. Our architecture is trained on a fully-connected CNN that uses features extracted from a training set. We evaluate the model on three large-scale datasets, including 3D facial images and 2D face images. We show that our model learns to extract features from two types of data: 3D human gaze images and 2D face images. The two types of data are captured in different time steps, which makes our architecture competitive in retrieval task. The architecture achieves superior retrieval performance compared to our current state-of-the-art model while maintaining a high temporal resolution.

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The Power of Adversarial Examples for Learning Deep Models

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  • A Theoretical Analysis of Online Learning: Some Properties and Experiments

    Deep Spatio-Temporal Learning of Motion RepresentationsThe problem of performing temporal matching is one of high importance in many applications such as visual search, face recognition and image processing. Due to the low temporal precision of the data, it is hard to compare features. We present a new neural network architecture, which uses a Convolutional Neural Network (CNN) for retrieval of face images as a basis. Our architecture is trained on a fully-connected CNN that uses features extracted from a training set. We evaluate the model on three large-scale datasets, including 3D facial images and 2D face images. We show that our model learns to extract features from two types of data: 3D human gaze images and 2D face images. The two types of data are captured in different time steps, which makes our architecture competitive in retrieval task. The architecture achieves superior retrieval performance compared to our current state-of-the-art model while maintaining a high temporal resolution.


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