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Regularization

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Papers

T-vMF Similarity for Regularizing Intra-Class Feature Distribution CVPR 2021
Local Regularizer Improves Generalization AAAI 2020
Feature Variance Regularization: A Simple Way to Improve the Generalizability of Neural Networks AAAI 2020
Beyond Dropout: Feature Map Distortion to Regularize Deep Neural Networks AAAI 2020
Network as Regularization for Training Deep Neural Networks: Framework, Model and Performance AAAI 2020
Regional Tree Regularization for Interpretability in Deep Neural Networks AAAI 2020
Group-Wise Dynamic Dropout Based on Latent Semantic Variations AAAI 2020
On the Regularization Properties of Structured Dropout CVPR 2020
Regularization on Spatio-Temporally Smoothed Feature for Action Recognition CVPR 2020
Regularizing CNN Transfer Learning With Randomised Regression CVPR 2020
Consistency Regularization for Certified Robustness of Smoothed Classifiers NIPS 2020
STEER : Simple Temporal Regularization For Neural ODE NIPS 2020
Explicit Regularisation in Gaussian Noise Injections NIPS 2020
MMA Regularization: Decorrelating Weights of Neural Networks by Maximizing the Minimal Angles NIPS 2020
Learning Representations from Imperfect Time Series Data via Tensor Rank Regularization ACL 2019
MixUp as Locally Linear Out-of-Manifold Regularization AAAI 2019
Weighted Channel Dropout for Regularization of Deep Convolutional Neural Network AAAI 2019
Robustness via Curvature Regularization, and Vice Versa CVPR 2019
Learning Not to Learn: Training Deep Neural Networks With Biased Data CVPR 2019
Learning towards Minimum Hyperspherical Energy NIPS 2018
Supervised autoencoders: Improving generalization performance with unsupervised regularizers NIPS 2018
Regularization Learning Networks: Deep Learning for Tabular Datasets NIPS 2018
Can We Gain More from Orthogonality Regularizations in Training Deep Networks? NIPS 2018
LDMNet: Low Dimensional Manifold Regularized Neural Networks CVPR 2018
Improving Neural Language Models with Weight Norm Initialization and Regularization EMNLP 2018