HEM: a margin-based loss for visual categorisation tasks | Paper Citation Record

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HEM: a margin-based loss for visual categorisation tasks and Mian, A

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HEM: a margin-based loss for visual categorisation tasks Concrete Problems in AI Safety

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HEM: a margin-based loss for visual categorisation tasks Loss Functions in the Era of Semantic Segmentation: A Survey and Outlook

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HEM: a margin-based loss for visual categorisation tasks Breaking Down Out-of-Distribution Detection: Many Methods Based on OOD Training Data Estimate a Combination of the Same Core Quantities

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HEM: a margin-based loss for visual categorisation tasks S., Malhotra, G., Dujmovi \'c , M., Montero, M

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HEM: a margin-based loss for visual categorisation tasks J., Fauqueur, J., and Cipolla, R

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HEM: a margin-based loss for visual categorisation tasks Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss

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HEM: a margin-based loss for visual categorisation tasks On Tiny Episodic Memories in Continual Learning

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HEM: a margin-based loss for visual categorisation tasks WDiscOOD: Out-of-Distribution Detection via Whitened Linear Discriminant Analysis

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HEM: a margin-based loss for visual categorisation tasks Average of Pruning: Improving Performance and Stability of Out-of-Distribution Detection

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HEM: a margin-based loss for visual categorisation tasks Deep Learning for Classical Japanese Literature

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HEM: a margin-based loss for visual categorisation tasks The Cityscapes Dataset for Semantic Urban Scene Understanding

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HEM: a margin-based loss for visual categorisation tasks and Singer, Y

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HEM: a margin-based loss for visual categorisation tasks Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks

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HEM: a margin-based loss for visual categorisation tasks Decoupled Kullback-Leibler Divergence Loss

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HEM: a margin-based loss for visual categorisation tasks Class-Balanced Loss Based on Effective Number of Samples

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HEM: a margin-based loss for visual categorisation tasks N., Bellinger, C., Roberts, M., and Chawla, N

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HEM: a margin-based loss for visual categorisation tasks An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

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HEM: a margin-based loss for visual categorisation tasks Shortcut Learning in Deep Neural Networks

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HEM: a margin-based loss for visual categorisation tasks Generalisation in humans and deep neural networks

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HEM: a margin-based loss for visual categorisation tasks Deep Residual Learning for Image Recognition

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HEM: a margin-based loss for visual categorisation tasks Identity Mappings in Deep Residual Networks

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HEM: a margin-based loss for visual categorisation tasks Bag of Tricks for Image Classification with Convolutional Neural Networks

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HEM: a margin-based loss for visual categorisation tasks Scaling Out-of-Distribution Detection for Real-World Settings

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HEM: a margin-based loss for visual categorisation tasks Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

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HEM: a margin-based loss for visual categorisation tasks A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks

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HEM: a margin-based loss for visual categorisation tasks Deep Anomaly Detection with Outlier Exposure

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HEM: a margin-based loss for visual categorisation tasks Natural Adversarial Examples

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HEM: a margin-based loss for visual categorisation tasks PixMix: Dreamlike Pictures Comprehensively Improve Safety Measures

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HEM: a margin-based loss for visual categorisation tasks Searching for MobileNetV3

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HEM: a margin-based loss for visual categorisation tasks MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

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HEM: a margin-based loss for visual categorisation tasks Densely Connected Convolutional Networks

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HEM: a margin-based loss for visual categorisation tasks Adversarial Examples Are Not Bugs, They Are Features

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HEM: a margin-based loss for visual categorisation tasks Training a Vision Transformer from scratch in less than 24 hours with 1 GPU

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HEM: a margin-based loss for visual categorisation tasks Less-forgetting Learning in Deep Neural Networks

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HEM: a margin-based loss for visual categorisation tasks One-vs-the-Rest Loss to Focus on Important Samples in Adversarial Training

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HEM: a margin-based loss for visual categorisation tasks Adversarial Logit Pairing

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HEM: a margin-based loss for visual categorisation tasks Torchattacks: A PyTorch Repository for Adversarial Attacks

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HEM: a margin-based loss for visual categorisation tasks Adam: A Method for Stochastic Optimization

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HEM: a margin-based loss for visual categorisation tasks Panoptic Feature Pyramid Networks

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HEM: a margin-based loss for visual categorisation tasks A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., Hassabis, D., Clopath, C., Kumaran, D., and Hadsell, R

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HEM: a margin-based loss for visual categorisation tasks Are DNNs fooled by extremely unrecognizable images?

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HEM: a margin-based loss for visual categorisation tasks M., Salakhutdinov, R., and Tenenbaum, J

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HEM: a margin-based loss for visual categorisation tasks Gradient-Based Adversarial and Out-of-Distribution Detection

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HEM: a margin-based loss for visual categorisation tasks Learning without Forgetting

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HEM: a margin-based loss for visual categorisation tasks Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

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HEM: a margin-based loss for visual categorisation tasks Avalanche: an End-to-End Library for Continual Learning

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HEM: a margin-based loss for visual categorisation tasks Metric Learning for Adversarial Robustness

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HEM: a margin-based loss for visual categorisation tasks The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence

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HEM: a margin-based loss for visual categorisation tasks Long-tail learning via logit adjustment

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Reference 66

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Reference 69

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Reference 74

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Reference 76

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Reference 77

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Reference 78

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Reference 80

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Reference 81

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HEM: a margin-based loss for visual categorisation tasks Consistency Regularization for Adversarial Robustness

Reference 82

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Reference 83

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Reference 84

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Reference 85

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Reference 86

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Reference 87

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Reference 88

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Reference 89

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Reference 90

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HEM: a margin-based loss for visual categorisation tasks Aggregated Residual Transformations for Deep Neural Networks

Reference 91

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Reference 92

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HEM: a margin-based loss for visual categorisation tasks OpenOOD: Benchmarking Generalized Out-of-Distribution Detection

Reference 93

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Reference 94

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Reference 95

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Reference 96

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Reference 97

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Reference 98

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Reference 99

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Reference 100

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