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Found 13 papers, 3 papers with code
VCC-INFUSE: Towards Accurate and Efficient Selection of Unlabeled Examples in Semi-supervised Learning
VCC is an universal plugin for SSL confidence calibration, using a variational autoencoder to select more accurate pseudo labels based on three types of consistency scores.
Target specific peptide design using latent space approximate trajectory collector
Despite the prevalence and many successes of deep learning applications in de novo molecular design, the problem of peptide generation targeting specific proteins remains unsolved.
Efficient Meta-Learning for Continual Learning with Taylor Expansion Approximation
However, they still suffer from the catastrophic forgetting problem in the setting of continual learning, since the past data of previous tasks are no longer available.
Unsupervised Domain Adaptation with Histogram-gated Image Translation for Delayered IC Image Analysis
To address the domain shift problem, we propose Histogram-gated Image Translation (HGIT), an unsupervised domain adaptation framework which transforms images from a given source dataset to the domain of a target dataset, and utilize the transformed images for training a segmentation network.
Regularizing Deep Neural Networks with Stochastic Estimators of Hessian Trace
In this paper, we develop a novel regularization method for deep neural networks by penalizing the trace of Hessian.
Understanding CNNs from excitations
To organize these excitations into final saliency maps, we introduce a double-chain backpropagation procedure.
Boosting Image Outpainting with Semantic Layout Prediction
Secondly, another GAN model is trained to synthesize real images based on the extended semantic layouts.
Principal Gradient Direction and Confidence Reservoir Sampling for Continual Learning
In Principal Gradient Direction, we optimize a target gradient that not only represents the major contribution of past gradients, but also retains the new knowledge of the current gradient.
A New Adaptive Gradient Method with Gradient Decomposition
Therefore, DecGD gets a rapid convergence in the early phases of training and controls the effective learning rates according to the loss based vectors which help lead to a better generalization.
Intra-Model Collaborative Learning of Neural Networks
Moreover, our method is proven to be robust to label noise with experiments on Cifar-10 dataset.
A New Variant of Stochastic Heavy ball Optimization Method for Deep Learning
Stochastic momentum optimization methods, also known as stochastic heavy ball (SHB) methods, are one of the most popular optimization methods for deep learning.
TopologyGAN: Topology Optimization Using Generative Adversarial Networks Based on Physical Fields Over the Initial Domain
In topology optimization using deep learning, load and boundary conditions represented as vectors or sparse matrices often miss the opportunity to encode a rich view of the design problem, leading to less than ideal generalization results.
MarginGAN: Adversarial Training in Semi-Supervised Learning
The new feature is that the classifier attempts to increase the margin of real examples and to decrease the margin of fake examples.
