Combining Input Discretisation with Adversarial Training 54
Adversarial Training for Relation Extraction
... in input signals. Adversarial examples (Szegedy et ...model. Adversarial training (Goodfel- low et ...applied adversarial training on straight- forward classification tasks, ...
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Domain-Adversarial Training of Neural Networks
... We perform this experiment on the same Amazon reviews data set described in the previous subsection. For each source-target domain pair, we generate the mSDA represen- tations using a corruption probability of 50% and a ...
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Generating Steganographic Images via Adversarial Training
... Given an input X, Eve outputs the probability, p, that X = C. Alice tries to learn an embedding scheme such that Eve always outputs p = 1 2 . We do not train Eve to maximize her prediction error, since she can ...
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Adversarial Training for Cross Domain Universal Dependency Parsing
... 1 We were not aware of the jack-knifed training data pro- vided by the organizer at submission time. first predicts the best unlabeled dependency struc- ture, and then assigns a label to each predicted arc with ...
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ARAML: A Stable Adversarial Training Framework for Text Generation
... no input to evaluate the performance of discrete GANs, and we followed the existing works to preprocess these datasets (Shi et ...our training/test ...
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Answer based Adversarial Training for Generating Clarification Questions
... There are several avenues of future work. Fol- lowing Mostafazadeh et al. (2016), we could com- bine text input with image input in the Amazon dataset (McAuley and Yang, 2016) to generate more relevant and ...
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Retrieval Enhanced Adversarial Training for Neural Response Generation
... Retrieval-Enhanced Adversarial Training (REAT) approach to make better use of N-best response ...with input messages are more likely to be seen as human-generated by the discriminator, which ...
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Adversarial Training for Satire Detection: Controlling for Confounding Variables
... the training time low. As mentioned before, we represent the input words with 300 di- mensional ...descent training with a batch size of 32 and alternating batches of the two branches of our ...
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Robust Neural Networks using Randomized Adversarial Training
... the input dimen- sion is low, the choice of the norm is of little importance because the ` ∞ and ` 2 balls overlap by a large margin, and the adversarial examples lie in the same ...-bounded ...
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Retinal image synthesis from multiple-landmarks input with generative adversarial networks
... Computer-aided diagnosis (CAD) systems benefit physicians in reducing workload and improving diagnostic accuracy for medical examination. Deep learning methods, espe- cially convolutional neural networks (CNNs), have ...
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Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing Their Input Gradients
... by training a model to have smooth input gradients with fewer extreme values, it will not only be more interpretable but also more resistant to adversarial ...smooth input gradients with ...
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Adversarial training for multi context joint entity and relation extraction
... Katiyar and Cardie (2017) investigate RNNs with attention without taking into account that relation labels are not mutually exclusive. Finally, Bek- oulis et al. (2018a) use LSTMs in a joint model for extracting just one ...
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GANomaly : semi-supervised anomaly detection via adversarial training.
... the input data representation and reconstructs the input image via the use of an encoder and a decoder network, ...an input image x, where x ∈ R w×h×c , and forward-passes it to its encoder network G ...
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AdvEntuRe: Adversarial Training for Textual Entailment with Knowledge Guided Examples
... To avoid this, we sub-sample our synthetic ex- amples to ensure that they are proportional to the input examples X , specifically they are bounded to ˛ j X j where ˛ is tuned for each dataset. Also, as seen in ...
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Robust Multilingual Part of Speech Tagging via Adversarial Training
... languages, adversarial training (AT) re- sults in cleaner word embedding distributions than the baseline, with a higher cosine similarity within each POS cluster, and with a clear advantage in the average ...
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Adversarial training with cycle consistency for unsupervised super-resolution in endomicroscopy
... have: Input image, proposed output, output from (Ravì et ...the input nor in the estimated HR images and this can eventually lead to a wrong clinical interpretation of the ...
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Generating input data for microstructure modelling: A deep learning approach using generative adversarial networks
... 4. Training of the MLA To gather input data for the training of the machine learning networks, EBSD pictures of the microstructure along the rolling direction of the thickness of the steel sheet were ...
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LexicalAT: Lexical Based Adversarial Reinforcement Training for Robust Sentiment Classification
... the input text, it does not augment the training data with new words and expressions, and thus limits the ro- bustness ...reinforcement training, lexicalAT is capable of learning the attacking policy ...
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Bilateral Adversarial Training: Towards Fast Training of More Robust Models Against Adversarial Attacks
... RS). Firstly we see that the target models trained by FGSM and LL suffer badly from the label leaking problem because the accuracy against FGSM attack is even higher than the clean accuracy. But this is just false ...
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On Stabilizing Generative Adversarial Training with Noise
... improved training, but as the noise variance approaches zero, the optimization problem converges to the original formulation and the algo- rithm may be subject to the usual unstable ...
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