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Found 8 papers, 0 papers with code
Beyond Examples: Constructing Explanation Space for Explaining Prototypes
As deep learning has been successfully deployed in diverse applications, there is ever increasing need for explaining its decision.
Distributional Perturbation for Efficient Exploration in Distributional Reinforcement Learning
Distributional reinforcement learning aims to learn distribution of return under stochastic environments.
Distribution-Driven Disjoint Prediction Intervals for Deep Learning
To address the issue, we propose a novel method that generates a union of disjoint PIs.
On the Convergence of Nonconvex Continual Learning with Adaptive Learning Rate
The memory based continual learning stores a small subset of the data for previous tasks and applies various methods such as quadratic programming and sample selection.
Nonconvex Continual Learning with Episodic Memory
We also show that memory-based approaches have an inherent problem of overfitting to memory, which degrades the performance on previously learned tasks, namely catastrophic forgetting.
Adversarial training with perturbation generator networks
However, these adversarial attack methods used in these techniques are fixed, making the model stronger only to attacks used in training, which is widely known as an overfitting problem.
Unsupervised classification into unknown number of classes
We propose a novel unsupervised classification method based on graph Laplacian.
Generative Adversarial Trainer: Defense to Adversarial Perturbations with GAN
The generator network generates an adversarial perturbation that can easily fool the classifier network by using a gradient of each image.
