9 citations · 14 across the 11 of their papers we have counts for
7 papers · 1 filter
ViPer: Visual Personalization of Generative Models via Individual Preference Learning
Sogand Salehi, Mahdi Shafiei, Teresa Yeo +2
Different users find different images generated for the same prompt desirable. This gives rise to personalized image generation which involves creating images aligned with an indiv…
Controlled Training Data Generation with Diffusion Models
Teresa Yeo, Andrei Atanov, Harold Benoit +4
We present a method to control a text-to-image generative model to produce training data useful for supervised learning. Unlike previous works that employ an open-loop approach and…
4M: Massively Multimodal Masked Modeling
David Mizrahi, Roman Bachmann, Oğuzhan Fatih Kar +4
Current machine learning models for vision are often highly specialized and limited to a single modality and task. In contrast, recent large language models exhibit a wide range of…
Rapid Network Adaptation: Learning to Adapt Neural Networks Using Test-Time Feedback
Teresa Yeo, Oğuzhan Fatih Kar, Zahra Sodagar +1
We propose a method for adapting neural networks to distribution shifts at test-time. In contrast to training-time robustness mechanisms that attempt to anticipate and counter the…
3D Common Corruptions and Data Augmentation
Oğuzhan Fatih Kar, Teresa Yeo, Andrei Atanov +1
We introduce a set of image transformations that can be used as corruptions to evaluate the robustness of models as well as data augmentation mechanisms for training neural network…
Robustness via Cross-Domain Ensembles
Teresa Yeo, Oğuzhan Fatih Kar, Alexander Sax +1
We present a method for making neural network predictions robust to shifts from the training data distribution. The proposed method is based on making predictions via a diverse set…