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Found 10 papers, 5 papers with code
Adversarial Sub-sequence for Text Generation
It is easy to improve the existing GAN-based models with this mechanism.
The Detection of Distributional Discrepancy for Text Generation
In this paper, we theoretically propose two metric functions to measure the distributional difference between real text and generated text.
Adding A Filter Based on The Discriminator to Improve Unconditional Text Generation
To alleviate the exposure bias, generative adversarial networks (GAN) use the discriminator to update the generator's parameters directly, but they fail by being evaluated precisely.
Distributional Discrepancy: A Metric for Unconditional Text Generation
The purpose of unconditional text generation is to train a model with real sentences, then generate novel sentences of the same quality and diversity as the training data.
Dynamic Defense Approach for Adversarial Robustness in Deep Neural Networks via Stochastic Ensemble Smoothed Model
The defense strategies are particularly passive in these processes, and enhancing initiative of such strategies can be an effective way to get out of this arms race.
The Diversity Metrics of Sub-models based on SVD of Jacobians for Ensembles Adversarial Robustness
A new constraints norm is proposed in model training based on these criteria to isolate adversarial transferability without any prior knowledge of adversarial samples.
Application of Machine Learning Methods in Inferring Surface Water Groundwater Exchanges using High Temporal Resolution Temperature Measurements
The results indicate that both ML and DL methods can be used to infer the surface/ground exchange flux.
Dynamic ensemble selection based on Deep Neural Network Uncertainty Estimation for Adversarial Robustness
The deep neural network has attained significant efficiency in image recognition.
Tactics2D: A Reinforcement Learning Environment Library with Generative Scenarios for Driving Decision-making
For access to the source code and participation in discussions, visit the official GitHub page for Tactcis2D at https://github. com/WoodOxen/Tactics2D.
SAM4UDASS: When SAM Meets Unsupervised Domain Adaptive Semantic Segmentation in Intelligent Vehicles
It involves Semantic-Guided Mask Labeling, which assigns semantic labels to unlabeled SAM masks using UDA pseudo-labels.
