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Found 17 papers, 0 papers with code
Fast Deterministic CUR Matrix Decomposition with Accuracy Assurance
This is because coordinate descent iteratively updates all the parameters in the objective until convergence.
Adaptive Random Feature Regularization on Fine-tuning Deep Neural Networks
To this end, AdaRand minimizes the gap between feature vectors and random reference vectors that are sampled from class conditional Gaussian distributions.
Adversarial Finetuning with Latent Representation Constraint to Mitigate Accuracy-Robustness Tradeoff
This paper addresses the tradeoff between standard accuracy on clean examples and robustness against adversarial examples in deep neural networks (DNNs).
Fast Regularized Discrete Optimal Transport with Group-Sparse Regularizers
Regularized discrete optimal transport (OT) is a powerful tool to measure the distance between two discrete distributions that have been constructed from data samples on two different domains.
Fast Saturating Gate for Learning Long Time Scales with Recurrent Neural Networks
We prove that the gradient vanishing of the gate function can be mitigated by accelerating the convergence of the saturating function, i. e., making the output of the function converge to 0 or 1 faster.
One-vs-the-Rest Loss to Focus on Important Samples in Adversarial Training
This paper proposes a new loss function for adversarial training.
Transfer Learning with Pre-trained Conditional Generative Models
To transfer source knowledge without these assumptions, we propose a transfer learning method that uses deep generative models and is composed of the following two stages: pseudo pre-training (PP) and pseudo semi-supervised learning (P-SSL).
F-Drop&Match: GANs with a Dead Zone in the High-Frequency Domain
The key idea of F-Drop is to filter out unnecessary high-frequency components from the input images of the discriminators.
Smoothness Analysis of Adversarial Training
We reveal that the constraint of adversarial attacks is one cause of the non-smoothness and that the smoothness depends on the types of the constraints.
Adversarial Training Makes Weight Loss Landscape Sharper in Logistic Regression
We theoretically and experimentally confirm that the weight loss landscape becomes sharper as the magnitude of the noise of adversarial training increases in the linear logistic regression model.
Constraining Logits by Bounded Function for Adversarial Robustness
We theoretically and empirically reveal that small logits by addition of a common activation function, e. g., hyperbolic tangent, do not improve adversarial robustness since input vectors of the function (pre-logit vectors) can have large norms.
Image Enhanced Rotation Prediction for Self-Supervised Learning
The rotation prediction (Rotation) is a simple pretext-task for self-supervised learning (SSL), where models learn useful representations for target vision tasks by solving pretext-tasks.
Effective Data Augmentation with Multi-Domain Learning GANs
When trained on each target dataset reduced the samples to 5, 000 images, Domain Fusion achieves better classification accuracy than the data augmentation using fine-tuned GANs.
Absum: Simple Regularization Method for Reducing Structural Sensitivity of Convolutional Neural Networks
Furthermore, we reveal that robust CNNs with Absum are more robust against transferred attacks due to decreasing the common sensitivity and against high-frequency noise than standard regularization methods.
Autoencoding Binary Classifiers for Supervised Anomaly Detection
Since our approach becomes able to reconstruct the normal data points accurately and fails to reconstruct the known and unknown anomalies, it can accurately discriminate both known and unknown anomalies from normal data points.
Sigsoftmax: Reanalysis of the Softmax Bottleneck
On the basis of this analysis, we propose sigsoftmax, which is composed of a multiplication of an exponential function and sigmoid function.
Preventing Gradient Explosions in Gated Recurrent Units
This problem is caused by an abrupt change in the dynamics of the GRU due to a small variation in the parameters.
