Search Results for author:
Found 8 papers, 4 papers with code
Blue noise for diffusion models
In this paper, we introduce a novel and general class of diffusion models taking correlated noise within and across images into account.
Scaling Vision Transformers to 22 Billion Parameters
The scaling of Transformers has driven breakthrough capabilities for language models.
Ranked #1 on
Zero-Shot Transfer Image Classification
on ObjectNet
CUF: Continuous Upsampling Filters
Neural fields have rapidly been adopted for representing 3D signals, but their application to more classical 2D image-processing has been relatively limited.
Proper Reuse of Image Classification Features Improves Object Detection
A common practice in transfer learning is to initialize the downstream model weights by pre-training on a data-abundant upstream task.
Impact of Aliasing on Generalization in Deep Convolutional Networks
We investigate the impact of aliasing on generalization in Deep Convolutional Networks and show that data augmentation schemes alone are unable to prevent it due to structural limitations in widely used architectures.
Bridging the Gap Between Adversarial Robustness and Optimization Bias
We demonstrate that the choice of optimizer, neural network architecture, and regularizer significantly affect the adversarial robustness of linear neural networks, providing guarantees without the need for adversarial training.
An Effective Anti-Aliasing Approach for Residual Networks
Image pre-processing in the frequency domain has traditionally played a vital role in computer vision and was even part of the standard pipeline in the early days of deep learning.
Data Augmentation for Skin Lesion Analysis
In this work, we investigate the impact of 13 data augmentation scenarios for melanoma classification trained on three CNNs (Inception-v4, ResNet, and DenseNet).
