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Fighting fire with fire: A spatial–frequency ensemble relation network with generative adversarial learning for adversarial image classification
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Ensemble Methods in Machine Learning
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An Ensemble of Epoch-Wise Empirical Bayes for Few-Shot Learning
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FeatMatch: Feature-Based Augmentation for Semi-supervised Learning
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Rethinking Few-Shot Image Classification: A Good Embedding is All You Need?
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TAFSSL: Task-Adaptive Feature Sub-Space Learning for Few-Shot Classification
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Incremental Few-Shot Meta-learning via Indirect Discriminant Alignment
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Embedding Propagation: Smoother Manifold for Few-Shot Classification
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A Simple Way to Make Neural Networks Robust Against Diverse Image Corruptions
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SEN: A Novel Feature Normalization Dissimilarity Measure for Prototypical Few-Shot Learning Networks
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Selecting Relevant Features from a Multi-domain Representation for Few-Shot Classification
inferredcites →https://doi.org/10.1007/978-3-030-69538-5_29openalex-snapshot
OpenGAN: Open Set Generative Adversarial Networks
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Metric Learning for Large Scale Image Classification: Generalizing to New Classes at Near-Zero Cost
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A framework for sensitivity analysis of decision trees
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Metalearning: a survey of trends and technologies
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Few-shot learning with adaptively initialized task optimizer: a practical meta-learning approach
inferredcites →https://doi.org/10.1007/s11045-019-00686-zopenalex-snapshot
PAC-Bayesian framework based drop-path method for 2D discriminative convolutional network pruning
inferredcites →https://doi.org/10.1016/j.eng.2019.12.012openalex-snapshot
Adversarial Attacks and Defenses in Deep Learning
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Precise object detection using adversarially augmented local/global feature fusion
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Diversity creation methods: a survey and categorisation
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CCE: An ensemble architecture based on coupled ANN for solving multiclass problems
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RED-Nets: Redistribution Networks for Multi-View Classification
inferredcites →https://doi.org/10.1016/j.neucom.2019.12.015openalex-snapshot
Reduction of JPEG compression artifacts based on DCT coefficients prediction
inferredcites →https://doi.org/10.1016/j.neunet.2020.07.031openalex-snapshot
MetalGAN: Multi-domain label-less image synthesis using cGANs and meta-learning
inferredcites →https://doi.org/10.1016/s0893-6080(02)00228-9openalex-snapshot
Meta-learning in Reinforcement Learning
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On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
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A Perspective View and Survey of Meta-Learning
inferredcites →https://doi.org/10.1038/s41467-020-17540-7openalex-snapshot
Synchronization of complex human networks
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An integrated iterative annotation technique for easing neural network training in medical image analysis
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An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression
inferredcites →https://doi.org/10.1109/access.2018.2807385openalex-snapshot
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
inferredcites →https://doi.org/10.1109/access.2018.2810849openalex-snapshot
Improvement of Generalization Ability of Deep CNN via Implicit Regularization in Two-Stage Training Process
inferredcites →https://doi.org/10.1109/cvpr.2016.308openalex-snapshot
Rethinking the Inception Architecture for Computer Vision
inferredcites →https://doi.org/10.1109/cvpr.2016.575openalex-snapshot
Synthesized Classifiers for Zero-Shot Learning
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/cvpr.2017.195openalex-snapshot
Xception: Deep Learning with Depthwise Separable Convolutions
inferredcites →https://doi.org/10.1109/cvpr.2017.243openalex-snapshot
Densely Connected Convolutional Networks
inferredcites →https://doi.org/10.1109/cvpr.2018.00291openalex-snapshot
CondenseNet: An Efficient DenseNet Using Learned Group Convolutions
inferredcites →https://doi.org/10.1109/cvpr.2018.00465openalex-snapshot
Generative Adversarial Perturbations
inferredcites →https://doi.org/10.1109/cvpr.2018.00564openalex-snapshot
Crowd Counting with Deep Negative Correlation Learning
inferredcites →https://doi.org/10.1109/cvpr.2018.00907openalex-snapshot
Learning Transferable Architectures for Scalable Image Recognition
inferredcites →https://doi.org/10.1109/cvpr.2019.00009openalex-snapshot
Finding Task-Relevant Features for Few-Shot Learning by Category Traversal
inferredcites →https://doi.org/10.1109/cvpr.2019.00049openalex-snapshot
Meta-Transfer Learning for Few-Shot Learning
inferredcites →https://doi.org/10.1109/cvpr42600.2020.00041openalex-snapshot
What It Thinks Is Important Is Important: Robustness Transfers Through Input Gradients
inferredcites →https://doi.org/10.1109/cvpr42600.2020.00049openalex-snapshot
One Man’s Trash Is Another Man’s Treasure: Resisting Adversarial Examples by Adversarial Examples
inferredcites →https://doi.org/10.1109/cvpr42600.2020.00090openalex-snapshot
Adversarial Examples Improve Image Recognition
inferredcites →https://doi.org/10.1109/cvpr42600.2020.00419openalex-snapshot
Adaptive Subspaces for Few-Shot Learning
inferredcites →https://doi.org/10.1109/cvpr42600.2020.00468openalex-snapshot
Learning to Select Base Classes for Few-Shot Classification
inferredcites →https://doi.org/10.1109/cvpr42600.2020.00883openalex-snapshot
Few-Shot Learning via Embedding Adaptation With Set-to-Set Functions
inferredcites →https://doi.org/10.1109/cvpr42600.2020.01070openalex-snapshot
Self-Training With Noisy Student Improves ImageNet Classification
inferredcites →https://doi.org/10.1109/cvpr42600.2020.01222openalex-snapshot
DeepEMD: Few-Shot Image Classification With Differentiable Earth Mover’s Distance and Structured Classifiers
inferredcites →https://doi.org/10.1109/cvpr42600.2020.01257openalex-snapshot
Learning to Learn Single Domain Generalization
inferredcites →https://doi.org/10.1109/cvpr42600.2020.01285openalex-snapshot
Instance Credibility Inference for Few-Shot Learning
inferredcites →https://doi.org/10.1109/cvpr42600.2020.01287openalex-snapshot
TransMatch: A Transfer-Learning Scheme for Semi-Supervised Few-Shot Learning
inferredcites →https://doi.org/10.1109/cvpr42600.2020.01348openalex-snapshot
Adversarial Feature Hallucination Networks for Few-Shot Learning
inferredcites →https://doi.org/10.1109/cvprw50498.2020.00386openalex-snapshot
SmoothMix: a Simple Yet Effective Data Augmentation to Train Robust Classifiers
inferredcites →https://doi.org/10.1109/icassp40776.2020.9053112openalex-snapshot
Meta Learning for End-To-End Low-Resource Speech Recognition
inferredcites →https://doi.org/10.1109/icassp40776.2020.9053759openalex-snapshot
Flexibly-tunable bitcube-based perceptual encryption within jpeg compression
inferredcites →https://doi.org/10.1109/iccv.2019.00283openalex-snapshot
Adversarial Defense via Learning to Generate Diverse Attacks
inferredcites →https://doi.org/10.1109/iccv48922.2021.00823openalex-snapshot
The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization
inferredcites →https://doi.org/10.1109/icnn.1996.548872openalex-snapshot
Generalization error of ensemble estimators
inferredcites →https://doi.org/10.1109/icpr48806.2021.9412539openalex-snapshot
A Self-supervised GAN for Unsupervised Few-shot Object Recognition
inferredcites →https://doi.org/10.1109/sp.2017.49openalex-snapshot
Towards Evaluating the Robustness of Neural Networks
inferredcites →https://doi.org/10.1109/tcyb.2020.2964566openalex-snapshot
Genetic Programming With a New Representation to Automatically Learn Features and Evolve Ensembles for Image Classification
inferredcites →https://doi.org/10.1109/tetci.2020.2991774openalex-snapshot
Loss Functions of Generative Adversarial Networks (GANs): Opportunities and Challenges
inferredcites →https://doi.org/10.1109/tkde.2020.3004939openalex-snapshot
Many-Class Few-Shot Learning on Multi-Granularity Class Hierarchy
inferredcites →https://doi.org/10.1109/tmm.2018.2889563openalex-snapshot
A Deep Neural Framework for Continuous Sign Language Recognition by Iterative Training
inferredcites →https://doi.org/10.1109/tnnls.2019.2935802openalex-snapshot
Guest Editorial Special Issue on Discriminative Learning for Model Optimization and Statistical Inference
inferredcites →https://doi.org/10.1109/tpami.2014.2366129openalex-snapshot
Meta-Parameter Free Unsupervised Sparse Feature Learning
inferredcites →https://doi.org/10.1109/tpami.2019.2943860openalex-snapshot
Nonlinear Regression via Deep Negative Correlation Learning
inferredcites →https://doi.org/10.1109/wacv45572.2020.9093623openalex-snapshot
Class-Discriminative Feature Embedding For Meta-Learning based Few-Shot Classification
inferredcites →https://doi.org/10.1109/wacv48630.2021.00270openalex-snapshot
Improving Few-Shot Learning using Composite Rotation based Auxiliary Task
inferredcites →https://doi.org/10.1142/9789814271073openalex-snapshot
Pattern Classification Using Ensemble Methods
inferredcites →https://doi.org/10.1145/3394171.3413811openalex-snapshot
Memory-Augmented Relation Network for Few-Shot Learning
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Adversarial Machine Learning in Image Classification: A Survey Toward the Defender’s Perspective
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A Full Stage Data Augmentation Method in Deep Convolutional Neural Network for Natural Image Classification
inferredcites →https://doi.org/10.1609/aaai.v31i1.11231openalex-snapshot
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
inferredcites →https://doi.org/10.1609/aaai.v34i04.6004openalex-snapshot
Ensembles of Locally Independent Prediction Models
inferredcites →https://doi.org/10.1609/aaai.v34i04.6069openalex-snapshot
Meta-Learning for Generalized Zero-Shot Learning
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A Review of Recent Research in Metareasoning and Metalearning
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Popular Ensemble Methods: An Empirical Study
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Pretrained Transformers Improve Out-of-Distribution Robustness
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Generating Adversarial Examples with Adversarial Networks
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Layer-wise learning based stochastic gradient descent method for the optimization of deep convolutional neural network
inferredcites →https://doi.org/10.4230/oasics.dx.2026.15openalex-snapshot
AutoResearch Lite: a reproducible paper-inspired experiment runner (Adam mechanism reproduction and convergence-speed comparison)
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Very Deep Convolutional Networks for Large-Scale Image Recognition
inferredcites →https://doi.org/10.48550/arxiv.1811.12231openalex-snapshot
ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
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Iterative annotation to ease neural network training: Specialized machine learning in medical image analysis
inferredcites →https://doi.org/10.48550/arxiv.1903.12261openalex-snapshot
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
inferredcites →https://doi.org/10.48550/arxiv.1904.11486openalex-snapshot
Making Convolutional Networks Shift-Invariant Again
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Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
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[Re] Meta-learning with differentiable closed-form solvers
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Managing Diversity in Regression Ensembles
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Advances in Neural Information Processing Systems 14
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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