[PDF] Top 20 Toward robust deep neural networks

... that are semantically and statistically different from those in-distribution ones. For instance, a real-world image-based digit reader model may be exposed to an enormous types of images that do not contain any digit, in ... Voir le document complet

... synthesizer. Toward this goal, a prerequisite is the development a synthesizer that should i) produce intelligible speech, ii) run in real time, iii) depend on as few parameters as possible, and iv) be ... Voir le document complet

... of deep neural networks suitable for time series analysis, the recurrent neural networks (RNN; Hochreiter & Schmidhuber, 1997; Sherstinsky, 2020), allows for online (real-time) ... Voir le document complet

... of deep learn- ing in ad-hoc IR tasks as well as the representation learning approach of words surrounded by external ...within deep structure neural net- ...other deep neural network ... Voir le document complet

... to utilize different non-robust features, which can serve as a defense against transfer attacks. Another key insight from these experiments is that certain network architectures consistently learn more similar ... Voir le document complet

... highly robust regardless of the values of the other four hyperparameters: in all four figures, the dashed green curves are always placed higher than the dashed red curves ... Voir le document complet

... defenses, learning-based adversarial detection methods uti- lize adversarial examples during the training phase, but of the model used as a detector (e.g. [ 10 ]). Although both pop- ular and reliable, these methods ... Voir le document complet

... be robust to noisy ...Multimodal Deep Learning Approaches for Emotion Recogni- tion in Video” (Kahou et ...presents deep learning approaches for emotion recognition from video clips and provides an ... Voir le document complet

... in deep computer ...the deep learning age, it is now common to learn in a single model Fea- ture extraction and Heuristic grasp generation (Joseph Redmon and Angelova, 2015 ; Trottier, Giguère, and ... Voir le document complet

... optimizing deep convolutional neural networks (CNN) by 1) reducing the computational complexity and 2) improving classification performance for the task of transfer ... Voir le document complet

... IV. C ONCLUSION In this paper, we present a novel background subtraction algorithm based on convolutional neural networks (ConvNets). Rather than building a sophisticated background model to deal with ... Voir le document complet

... compact neural networks: deep networks in which all weight matrices are either diag- onal or circulant ...such networks with a large number of layers had not been done ...that ... Voir le document complet

... Received 18 July 2019 / Accepted 9 December 2019 ABSTRACT In this work, we propose two convolutional neural network classifiers for detecting contaminants in astronomical images. Once trained, our classifiers are ... Voir le document complet

... ORK Deep model for computer vision and natural ...the deep neural network developed rapidly in recent years in both the field of computer vision and natural lan- ...a deep Convolutional ... Voir le document complet

... standard Neural Nets, where the com- plexity of the model is dominated by the mere size of the weight vec- ...that deep networks actually depend on the sequential acquisition of different “skills”, ... Voir le document complet

... In addition, we compare these representations along with both long short-term memory networks (LSTM) and convolutional neural networks (CNN) for prediction of five i[r] ... Voir le document complet

... 1 Introduction The tremendous empirical success of deep neural networks (DNN) for many machine learning tasks such as image classification and object recognition (Krizhevsky et al., 2017) contrasts ... Voir le document complet

... with deep neural networks to automatically discriminate anesthetic states induced by ...tional neural networks significantly outperform multilayer perceptrons in dis- crimination ... Voir le document complet

... of neural networks approach has reunited them under the name of artificial intelligence in this recent ...this neural network approach has become the dominant method in the machine learning ... Voir le document complet

... Convolutional Neural Networks (CNN) has achieved a great success in image recognition task by automatically learning a hierarchical feature representation from raw ...the deep CNN ...existing ... Voir le document complet