Search Results for author:
Found 11 papers, 8 papers with code
Unlocking Intrinsic Fairness in Stable Diffusion
Recent text-to-image models like Stable Diffusion produce photo-realistic images but often show demographic biases.
Scaling Rectified Flow Transformers for High-Resolution Image Synthesis
Rectified flow is a recent generative model formulation that connects data and noise in a straight line.
DataComp: In search of the next generation of multimodal datasets
Multimodal datasets are a critical component in recent breakthroughs such as Stable Diffusion and GPT-4, yet their design does not receive the same research attention as model architectures or training algorithms.
The Role of Pre-training Data in Transfer Learning
We investigate the impact of pre-training data distribution on the few-shot and full fine-tuning performance using 3 pre-training methods (supervised, contrastive language-image and image-image), 7 pre-training datasets, and 9 downstream datasets.
REPAIR: REnormalizing Permuted Activations for Interpolation Repair
In this paper we look into the conjecture of Entezari et al. (2021) which states that if the permutation invariance of neural networks is taken into account, then there is likely no loss barrier to the linear interpolation between SGD solutions.
Studying the impact of magnitude pruning on contrastive learning methods
We study the impact of different pruning techniques on the representation learned by deep neural networks trained with contrastive loss functions.
Understanding the effect of sparsity on neural networks robustness
This paper examines the impact of static sparsity on the robustness of a trained network to weight perturbations, data corruption, and adversarial examples.
Deep Neural Network Pruning for Nuclei Instance Segmentation in Hematoxylin & Eosin-Stained Histological Images
We investigate the impact of weight pruning on the performance of both branches separately and on the final nuclei instance segmentation result.
The Role of Permutation Invariance in Linear Mode Connectivity of Neural Networks
In this paper, we conjecture that if the permutation invariance of neural networks is taken into account, SGD solutions will likely have no barrier in the linear interpolation between them.
Class-dependent Compression of Deep Neural Networks
Motivated by the success of the lottery ticket hypothesis, in this paper we propose an iterative deep model compression technique, which keeps the number of false negatives of the compressed model close to the one of the original model at the price of increasing the number of false positives if necessary.
AVID: Adversarial Visual Irregularity Detection
Real-time detection of irregularities in visual data is very invaluable and useful in many prospective applications including surveillance, patient monitoring systems, etc.
