153 citations · 247 across the 10 of their papers we have counts for
7 papers · 1 filter
From Snow to Rain: Evaluating Robustness, Calibration, and Complexity of Model-Based Robust Training
Josué Martínez-Martínez, Olivia Brown, Giselle Zeno +2
Robustness to natural corruptions remains a critical challenge for reliable deep learning, particularly in safety-sensitive domains. We study a family of model-based training appro…
LoRAX: LoRA eXpandable Networks for Continual Synthetic Image Attribution
Danielle Sullivan-Pao, Nicole Tian, Pooya Khorrami
As generative AI image technologies become more widespread and advanced, there is a growing need for strong attribution models. These models are crucial for verifying the authentic…
Meta-Learning and Self-Supervised Pretraining for Real World Image Translation
Ileana Rugina, Rumen Dangovski, Mark Veillette +4
Recent advances in deep learning, in particular enabled by hardware advances and big data, have provided impressive results across a wide range of computational problems such as co…
Seq-NMS for Video Object Detection
Wei Han, Pooya Khorrami, Tom Le Paine +6
Video object detection is challenging because objects that are easily detected in one frame may be difficult to detect in another frame within the same clip. Recently, there have b…
How Deep Neural Networks Can Improve Emotion Recognition on Video Data
Pooya Khorrami, Tom Le Paine, Kevin Brady +2
We consider the task of dimensional emotion recognition on video data using deep learning. While several previous methods have shown the benefits of training temporal neural networ…
Do Deep Neural Networks Learn Facial Action Units When Doing Expression Recognition?
Pooya Khorrami, Tom Le Paine, Thomas S. Huang
Despite being the appearance-based classifier of choice in recent years, relatively few works have examined how much convolutional neural networks (CNNs) can improve performance on…
An Analysis of Unsupervised Pre-training in Light of Recent Advances
Tom Le Paine, Pooya Khorrami, Wei Han +1
Convolutional neural networks perform well on object recognition because of a number of recent advances: rectified linear units (ReLUs), data augmentation, dropout, and large label…