Search Results for author:
Found 5 papers, 0 papers with code
Evaluating Co-Creativity using Total Information Flow
We propose a method to compute the information flow using pre-trained generative models as entropy estimators.
PosCUDA: Position based Convolution for Unlearnable Audio Datasets
Recent works such as CUDA propose solutions to this problem by adding class-wise blurs to make datasets unlearnable, i. e a model can never use the acquired dataset for learning.
Bias-Free FedGAN: A Federated Approach to Generate Bias-Free Datasets
Our approach generates metadata at the aggregator using the models received from clients and retrains the federated model to achieve bias-free results for image synthesis.
DPD-InfoGAN: Differentially Private Distributed InfoGAN
The Information Maximizing GAN (InfoGAN) is a variant of the default GAN that introduces feature-control variables that are automatically learned by the framework, hence providing greater control over the different kinds of images produced.
Deep Learning for Skin Lesion Classification
Our system learns to classify the images based on the model built using the training images given in the challenge and the experimental results were evaluated using validation and test sets.
