Deep Verifier Networks: Verification of Deep Discriminative Models with Deep Generative Models.

In this paper, we propose a novel framework -- deep verifier networks (DVN) to verify the inputs and outputs of deep discriminative models with deep generative models. ... Our verifier network is trained independently with the prediction model, which eliminates the need of retraining the verifier network for a new model. ... Hendrycks In this paper, we propose to verify the predictions of deep discriminative models by using deep generative models that try to generate the input conditioned on the label selected by the discriminative ...

arXiv:1911.07421v3 fatcat:abd5vyc4ibbzlonppa45xb77qa

Multiple Versions

Specifically, we generalize previous schemes into two critical design components in MetaV: the adaptive fingerprint and the meta-verifier, which are jointly optimized such that the meta-verifier learns ... Spanning classification, regression and generative modeling, extensive experimental results validate the substantially improved performance of MetaV over the state-of-the-art fingerprinting schemes and ... The first scenario covers the usage of deep convolutional neural network (CNN) for skin cancer diagnosis. ...

arXiv:2201.07391v3 fatcat:iw3evcdpzbdrfn2vvwtxbagcmi

Open Access Multiple Versions

We propose an integrated process for verifying artificial neural network (ANN) classifiers. This process consists of an off-line verification and an on-line performance prediction phase. ... The specification and verification of algorithms is vital for safety-critical autonomous systems which incorporate deep learning elements. ... A representation of the NRD associated with activation values generated within a network with just two neurons, n 1 and n 2 Figure 3 . 3 Figure 3. ...

doi:10.4018/ijaiml.289536 fatcat:hw2pfc5mfrfqdemqwmwrov3svi

We propose a deep semi-supervised model named SEmi-supervised VErification Network (SEVEN) to address these challenges. The model consists of two complementary components. ... Furthermore, SEVEN is competitive with fully supervised baselines trained with a larger amount of labeled data. It indicates the importance of the generative component in SEVEN. ... To address such challenges, we propose Deep SEmisupervised VErification Networks (SEVEN) that consists of a generative and a discriminative component to learn general and category specific representations ...

arXiv:1706.03692v2 fatcat:yxgh44aphvc2rompv5c5bxj6ua

Multiple Versions

We propose a deep semi-supervised model named SEmi-supervised VErification Network (SEVEN) to address these challenges. The model consists of two complementary components. ... Furthermore, SEVEN is competitive with fully supervised baselines trained with a larger amount of labeled data. It indicates the importance of the generative component in SEVEN. ... To address such challenges, we propose Deep SEmisupervised VErification Networks (SEVEN) that consists of a generative and a discriminative component to learn general and category specific representations ...

doi:10.24963/ijcai.2017/358 dblp:conf/ijcai/NorooziZBXY17 fatcat:sopmdc7acfe27bhcpz6f2vuqxq

A deep convolutional neural network (DCNN) model that is suitable for motor fault diagnosis is proposed, and the fault characteristics are learned from the original data layer by layer, so as to realize ... on generative adversarial network under unbalanced data sets. ... GAN is a kind of deep generation model that utilizes each other, mainly consisting of generating network G and discrimination network D [8] . ...

doi:10.12783/dtetr/amee2019/33455 fatcat:ddjjamkcq5bxbdp4zoao2jpozu

We overturn this problem by further fine-tuning the trained model using the Siamese framework for generating a discriminative feature space to distinguish between same and different speakers regardless ... In training phase, the network is trained to distinguish between different speaker identities for creating the background model. One of the crucial parts is to create the speaker models. ... Recently, with the advent of deep learning in different applications such as speech, image recognition and network pruning [1] - [4] , data-driven approaches using Deep Neural Networks (DNNs) have also ...

arXiv:1803.05427v2 fatcat:tfxbk7eeonavpiguru2cw54amm

Multiple Versions

In this paper, by employing the merits of deep metric learning, we propose a novel metric-based generative adversarial network (MBGAN), which uses the distance-criteria to distinguish between real and ... Specifically, the discriminator of MBGAN adopts a triplet structure and learns a deep nonlinear transformation, which maps input samples into a new feature space. ... [31] extended the idea of GAN with deep convolutional neural network by employing a set of architectural guidelines on the structure of current CNN model, such as replacing pooling with strided-convolution ...

doi:10.1145/3123266.3123334 dblp:conf/mm/DaiXF17 fatcat:3rxeqalfcjc4hakkprgdjltgqq

The second sub-network is a verifier that helps the generator to preserve the ID information during the generation process. ... Using a database of blurred fingerprints and corresponding ridge maps, the deep network learns to deblur from the input blurry samples. ... GANs are usually a pair of two networks, a generator and a discriminator. ...

arXiv:2106.11354v1 fatcat:wct7pqtj6najzeb7djeqmdhoxa

The main goal of end-to-end training is the model being optimized to be consistent with the speaker verification protocol. ... The LSTM architecture is trained to create a discrimination space for validating the match and non-match pairs for speaker verification. ... SPEAKER VERIFICATION USING DEEP NEURAL NETWORKS Here, we explain the speaker verification phases using deep learning. ...

arXiv:1805.00604v3 fatcat:k5zmtzdw3jfytebf4qo36bdpye

Multiple Versions

We construct Generator and Discriminator of the ACGAN by stacking multiple 1-dimensional convolutional layers with small kernel size. ... In order to evaluate the performance of augmentation model, a set of quantitative indicators are introduced to verify the quality of generated ECG signals. ... Classification model is designed to extract deep features from the ECGs. We adopt stacked residual network parallel connected with LSTM network as main frame to construct the classification model. ...

doi:10.1109/access.2019.2930882 fatcat:mijlqmn6xjbu3pcj5cs2ly5724

DOAJ

Our contributions are: (i) We propose a Deep Comparator Network (DCN) that can ingest a pair of sets (each may contain a variable number of images) as inputs, and compute a similarity between the pair- ... Instead, we design a neural network architecture that can directly learn set-wise verification. ... Acknowledgment This research is based upon work supported by the O ce of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via contract number 2014 ...

arXiv:1807.11440v1 fatcat:qbdcmk3q4vakjmpsilrt65vvly

Open Access

Reliable identification and verification of off-line handwritten signatures from images is a difficult problem with many practical applications. ... In this paper, we present a deep learning model for offline handwritten signature recognition which is able to extract high-level representations. ... Learning in Deep Neural Networks Definition 1. Deep Neural Network: A deep neural network contains an input layer and an output layer, separated by l layers of hidden units. ...

doi:10.1007/978-3-642-25085-9_62 fatcat:k57l4lc43vh6rbpuqcb53g3as4

A comparison of a number of different deep neural networks is presented. ... Experiments are performed using Long short-term memory (LSTM), bi-directional long short-term memory (BLSTM) and time delay neural networks (TDNN) with a Gaussian mixture model (GMM) being used to provide ... We have shown that a deep learning model outperforms a GMM model as well as producing results that improve upon the state of the art in liveness verification through the use of visual speech recognition ...

doi:10.20533/ijmip.2042.4647.2018.0047 fatcat:i4xpd7425vgg5lclzyijcequ6y

Moreover, the generator network is regularized with an additional face dataset and adversarial loss to decrease the overfitting of the limited samples. ... To extract robust features, we integrate a pre-trained face model to the kinship face generator. ... deep models [12, 23, 3] . ...

arXiv:1806.08600v2 fatcat:6q66aavlvjej3nislwxoou7p4m

Open Access Multiple Versions

Deep Verifier Networks: Verification of Deep Discriminative Models with Deep Generative Models [article]

Preserved Fulltext

Other Versions

MetaV: A Meta-Verifier Approach to Task-Agnostic Model Fingerprinting [article]

Preserved Fulltext

Other Versions

An Integrated Process for Verifying Deep Learning Classifiers Using Dataset Dissimilarity Measures

Preserved Fulltext

SEVEN: Deep Semi-supervised Verification Networks [article]

Preserved Fulltext

Other Versions

SEVEN: Deep Semi-supervised Verification Networks

Preserved Fulltext

Deep Convolution Neural Network Motor Fault Identification Based on Generative Adversarial Network under Unbalanced Sample

Preserved Fulltext

Speaker Verification using Convolutional Neural Networks [article]

Preserved Fulltext

Other Versions

Metric-based Generative Adversarial Network

Preserved Fulltext

FDeblur-GAN: Fingerprint Deblurring using Generative Adversarial Network [article]

Preserved Fulltext

Text-Independent Speaker Verification Using Long Short-Term Memory Networks [article]

Preserved Fulltext

Other Versions

ECG Arrhythmias Detection Using Auxiliary Classifier Generative Adversarial Network and Residual Network

Preserved Fulltext

Comparator Networks [article]

Preserved Fulltext

Deep Learning Networks for Off-Line Handwritten Signature Recognition [chapter]

Preserved Fulltext

Liveness Verification Using Deep Neural Network Based Visual Speech Recognition

Preserved Fulltext

KinshipGAN: Synthesizing of Kinship Faces From Family Photos by Regularizing a Deep Face Network [article]

Preserved Fulltext

Other Versions