13 papers with code • 0 benchmarks • 0 datasets
In this paper, we investigate the problem of overfitting in deep reinforcement learning.
Continual learning has received a great deal of attention recently with several approaches being proposed.
In this paper, we present an unsupervised image enhancement generative adversarial network (UEGAN), which learns the corresponding image-to-image mapping from a set of images with desired characteristics in an unsupervised manner, rather than learning on a large number of paired images.
We propose a deep collaborative weight-based classification (DeepCWC) method to resolve this problem, by providing a novel option to fully take advantage of deep features in classic machine learning.
We have developed convolutional neural networks (CNN) for a facial expression recognition task.
We propose a smooth kernel regularizer that encourages spatial correlations in convolution kernel weights.
There are existing efforts that model the training dynamics of GANs in the parameter space but the analysis cannot directly motivate practically effective stabilizing methods.
Ranked #19 on Image Generation on CIFAR-10 (Inception score metric)