6 accepted papers
"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 individual’s visual preference. Current generative models are, however, tuned to produce outputs that appeal to a broad audi…
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 capabilities, hinting at a possibility for similarly versatile models in computer vision. In this paper, we take a step…
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 the shift, we create a closed-loop system and make use of test-time feedback signal to adapt a network. We show that this loop can be ef…
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 networks. The primary distinction of the proposed transformations is that, unlike existing approaches such as Common Corruption…
When developing deep learning models, we usually decide what task we want to solve then search for a model that generalizes well on the task. An intriguing question would be: what if, instead of fixing the task and searching in the model space, we fix the model and search in the task space? Can we f…
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 of cues (called `middle domains') and ensembling them into one strong prediction. The premise of the idea is that predi…