Search Results for author:
Found 6 papers, 3 papers with code
Sanitized Clustering against Confounding Bias
Therein, the interested clustering factor and the confounding factor are coarsely considered in the raw feature space, where the correlation between the data and the confounding factor is ideally assumed to be linear for convenient solutions.
Earning Extra Performance from Restrictive Feedbacks
Unlike existing model tuning methods where the target data is always ready for calculating model gradients, the model providers in EXPECTED only see some feedbacks which could be as simple as scalars, such as inference accuracy or usage rate.
Robust Deep Learning Models Against Semantic-Preserving Adversarial Attack
In this paper, we study the robustness of deep learning models against joint perturbations by proposing a novel attack mechanism named Semantic-Preserving Adversarial (SPA) attack, which can then be used to enhance adversarial training.
TRIP: Refining Image-to-Image Translation via Rival Preferences
In particular, we simultaneously train two modules: a generator that translates an input image to the desired image with smooth subtle changes with respect to the interested attributes; and a ranker that ranks rival preferences consisting of the input image and the desired image.
Fine-Tuning from Limited Feedbacks
Instead of learning from scratch, fine-tuning a pre-trained model to fit a related target dataset of interest or downstream tasks has been a handy trick to achieve the desired performance.
Differential-Critic GAN: Generating What You Want by a Cue of Preferences
This paper proposes Differential-Critic Generative Adversarial Network (DiCGAN) to learn the distribution of user-desired data when only partial instead of the entire dataset possesses the desired property.
