Search Results for author:
Found 3 papers, 2 papers with code
Why does Knowledge Distillation Work? Rethink its Attention and Fidelity Mechanism
By increasing data augmentation strengths, our key findings reveal a decrease in the Intersection over Union (IoU) of attentions between teacher models, leading to reduced student overfitting and decreased fidelity.
Structural Similarity: When to Use Deep Generative Models on Imbalanced Image Dataset Augmentation
Our experiments on imbalanced image dataset classification show that, the validation accuracy improvement with such re-balancing method is related to the image similarity between different classes.
When do GANs replicate? On the choice of dataset size
With empirical evidence from BigGAN and StyleGAN2, on datasets CelebA, Flower and LSUN-bedroom, we show that dataset size and its complexity play an important role in GANs replication and perceptual quality of the generated images.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
