Free Adversarial Training with Layerwise Heuristic Learning.

Since the resulting learning rule seems to require little to no learning rate tuning, it may unlock a simpler workflow for training deeper and more complex neural networks. ... Class-conditional generative adversarial network training We train a class-conditional generative adversarial network with projection discriminator [33, 37] on the CIFAR-10 dataset [41] . ... Generative adversarial learning [32] trains a discriminator network D to classify data as real or fake, and a generator network G is trained to fool D. Competition drives learning in both networks. ...

arXiv:2002.03432v3 fatcat:uf6uovk4kjgr3db2vqjl5mje6e

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We tackle the question of model smoothness and show that it is a useful inductive bias which aids generalization, adversarial robustness, generative modeling and reinforcement learning. ... How sensitive should machine learning models be to input changes? ... -layerwise regularization applied to the entire space. ...

arXiv:2012.07969v3 fatcat:pa6ka7vtdfgz3d3czf5hmlgkb4

Open Access Multiple Versions

In this paper, the Adaptive Hierarchical Heuristic Mathematical Model (AHHMM) has been proposed for the deep learning approach. ... INDEX TERMS Lung cancer detection, deep learning, deep neural network, mathematical model. 86400 This work is licensed under a Creative Commons Attribution 4.0 License. ... The measured value is compared with the extracted and trained features for cancer classification based on deep learning. ...

doi:10.1109/access.2020.2992645 fatcat:xu7fgjwd7rallkx4hodxn4va5e

DOAJ

Deep Learning (DL) is vulnerable to out-of-distribution and adversarial examples resulting in incorrect outputs. ... The model employs a greedy layerwise training operation for unsupervised feature learning and supervised parameter tuning. ... The motivation is that while it is difficult to model every variant of anomaly distribution, one can learn effective heuristics for detecting OOD samples by exposing the model to diverse OOD datasets. ...

arXiv:2003.06979v2 fatcat:4mogo75b4rbxrc6vph2xmllkue

Multiple Versions

Deep Learning (DL) is vulnerable to out-of-distribution and adversarial examples resulting in incorrect outputs. ... The model employs a greedy layerwise training operation for unsupervised feature learning and supervised parameter tuning. ... The motivation is that while it is difficult to model every variant of anomaly distribution, one can learn effective heuristics for detecting OOD samples by exposing the model to diverse OOD datasets. ...

doi:10.1109/access.2020.3010274 fatcat:3xjpfc64nvcbtfpwtwwbitjuvm

DOAJ

Convex relaxations are effective for training and certifying neural networks against norm-bounded adversarial attacks, but they leave a large gap between certifiable and empirical robustness. ... We propose two regularizers that can be used to train neural networks that yield tighter convex relaxation bounds for robustness. ... In addition, adversarial training uses adversarial examples as opposed to clean examples during training, so that the network can learn how to classify arXiv:2002.09766v1 [cs.LG] 22 Feb 2020 adversarial ...

arXiv:2002.09766v1 fatcat:yli2k7rcpfgfvnw57b7r6eebvq

With adversarial learning, we build two discriminators whose inputs are respectively multi-level features and predicted segmentation masks. ... However, the generalization capability of deep models on test data with different distributions remain as a major challenge. ... Loss Functions and Training Strategies In adversarial learning, the DAM is pitted against an adversary with the above two discriminators. ...

arXiv:1812.07907v1 fatcat:muziwawywja45cjfhrpwqr7bwm

In some scenarios, classifier requires detecting out-of-distribution samples far from its training data. ... With desirable characteristics, reconstruction autoencoder-based methods deal with this problem by using input reconstruction error as a metric of novelty vs. normality. ... Many existing methods rely on training or tuning with data labelled as OoD from other categories [28, 32] , adversaries [15, 19] or the leave-out sub-set of training samples [30] . ...

arXiv:2203.02194v2 fatcat:hh7aurrw2relrequbowxekq65m

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Nero trains reliably without momentum or weight decay, works in situations where Adam and SGD fail, and requires little to no learning rate tuning. ... The paper concludes by discussing how this geometric connection between architecture and optimisation may impact theories of generalisation in deep learning. ... The Adam optimiser was used for training with a fixed learning rate of 0.01. ...

arXiv:2102.07227v2 fatcat:lprb6bbefvchtov4cwsyccdcy4

Multiple Versions

We propose a black-box algorithm called Adversarial Variational Inference and Learning (AdVIL) to perform inference and learning on a general Markov random field (MRF). ... On one hand, compared with contrastive divergence, AdVIL requires a minimal assumption about the model structure and can deal with a broader family of MRFs. ... The layerwise structure potentially benefits the training of both methods. ...

arXiv:1901.08400v3 fatcat:bao2bnestberbcbxi2qrsapq2q

Multiple Versions

Research to address these concerns is particularly active, with a significant number of papers released in the past few years. ... This survey paper conducts a review of the current research effort into making DNNs safe and trustworthy, by focusing on four aspects: verification, testing, adversarial attack and defence, and interpretability ... ., 2018] introduces ensemble adversarial training, which augments training data with perturbations transferred from other models. ...

arXiv:1812.08342v5 fatcat:awndtbca4jbi3pcz5y2d4ymoja

Multiple Versions

., 2018] introduces ensemble adversarial training, which augments training data with perturbations transferred from other models. ... Please note that, DeepFool is a heuristic algorithm for a neural network classifier that provides no guarantee to find the adversarial image with the minimum distortion, but in practise it is an very effective ...

doi:10.1016/j.cosrev.2020.100270 fatcat:biji56htvnglfhl7n3jnuelu2i

This makes monitoring and managing the multitude of network elements intractable with existing tools and impractical for traditional machine learning algorithms that rely on hand-crafted feature engineering ... In particular, deep learning based solutions can automatically extract features from raw data, without human expertise. ... with existing theoretical methods or heuristics. ...

arXiv:2011.05267v1 fatcat:yz2zp5hplzfy7h5kptmho7mbhe

Relevant saliency modeling trends with key issues, core techniques, and the scope for future research work have been discussed in the context of difficulties often faced in salient object detection. ... These methods can be broadly categorized into two categories based on their feature engineering mechanism: conventional or deep learning-based. ... Adversarial Training Based Models The Generative Adversarial Networks (GANs) has gained a lot of attention from researchers in fields such as image generation [212] , image super-resolution [213] and ...

doi:10.3390/e22101174 pmid:33286942 pmcid:PMC7597345 fatcat:3p5d2nal4vhxbi2via3g7oicga

DOAJ

Deep learning models hold state of the art performance in many fields, yet their design is still based on heuristics or grid search methods. ... Model compression is an active field of research that targets the problem of realizing deep learning models in hardware. ... Along with these differences, to the best of our knowledge, none of the prior works demonstrate a heuristic to optimize depth of a network. ...

arXiv:1812.06224v3 fatcat:xmxnzrhrsrd2df5gjbycj6lpau

Multiple Versions

On the distance between two neural networks and the stability of learning [article]

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A case for new neural network smoothness constraints [article]

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Deep Learning Assisted Predict of Lung Cancer on Computed Tomography Images using the Adaptive Hierarchical Heuristic Mathematical Model

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Anomalous Example Detection in Deep Learning: A Survey [article]

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Anomalous Example Detection in Deep Learning: A Survey

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Improving the Tightness of Convex Relaxation Bounds for Training Certifiably Robust Classifiers [article]

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PnP-AdaNet: Plug-and-Play Adversarial Domain Adaptation Network with a Benchmark at Cross-modality Cardiac Segmentation [article]

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Rethinking Reconstruction Autoencoder-Based Out-of-Distribution Detection [article]

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Learning by Turning: Neural Architecture Aware Optimisation [article]

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To Relieve Your Headache of Training an MRF, Take AdVIL [article]

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A Survey of Safety and Trustworthiness of Deep Neural Networks: Verification, Testing, Adversarial Attack and Defence, and Interpretability [article]

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A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability

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Deep Neural Mobile Networking [article]

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Salient Object Detection Techniques in Computer Vision—A Survey

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A Low Effort Approach to Structured CNN Design Using PCA [article]

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