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08/31/2023
Adversarial Finetuning with Latent Representation Constraint to Mitigate Accuracy-Robustness Tradeoff
This paper addresses the tradeoff between standard accuracy on clean exa...
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07/26/2023
Regularizing Neural Networks with Meta-Learning Generative Models
This paper investigates methods for improving generative data augmentati...
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03/14/2023
Fast Regularized Discrete Optimal Transport with Group-Sparse Regularizers
Regularized discrete optimal transport (OT) is a powerful tool to measur...
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10/04/2022
Fast Saturating Gate for Learning Long Time Scales with Recurrent Neural Networks
Gate functions in recurrent models, such as an LSTM and GRU, play a cent...
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07/21/2022
Switching One-Versus-the-Rest Loss to Increase the Margin of Logits for Adversarial Robustness
Defending deep neural networks against adversarial examples is a key cha...
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04/27/2022
Transfer Learning with Pre-trained Conditional Generative Models
Transfer learning is crucial in training deep neural networks on new tar...
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06/04/2021
F-Drop Match: GANs with a Dead Zone in the High-Frequency Domain
Generative adversarial networks built from deep convolutional neural net...
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03/02/2021
Smoothness Analysis of Loss Functions of Adversarial Training
Deep neural networks are vulnerable to adversarial attacks. Recent studi...
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02/05/2021
Adversarial Training Makes Weight Loss Landscape Sharper in Logistic Regression
Adversarial training is actively studied for learning robust models agai...
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10/06/2020
Constraining Logits by Bounded Function for Adversarial Robustness
We propose a method for improving adversarial robustness by addition of ...
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12/25/2019
Multiple Pretext-Task for Self-Supervised Learning via Mixing Multiple Image Transformations
Self-supervised learning is one of the most promising approaches to lear...
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12/25/2019
Effective Data Augmentation with Multi-Domain Learning GANs
For deep learning applications, the massive data development (e.g., coll...
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09/19/2019
Absum: Simple Regularization Method for Reducing Structural Sensitivity of Convolutional Neural Networks
We propose Absum, which is a regularization method for improving adversa...
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03/26/2019
Autoencoding Binary Classifiers for Supervised Anomaly Detection
We propose the Autoencoding Binary Classifiers (ABC), a novel supervised...
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05/28/2018