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Found 9 papers, 2 papers with code
Controlled Training Data Generation with Diffusion Models
In this work, we present a method to control a text-to-image generative model to produce training data specifically "useful" for supervised learning.
4M: Massively Multimodal Masked Modeling
Current machine learning models for vision are often highly specialized and limited to a single modality and task.
Rapid Network Adaptation: Learning to Adapt Neural Networks Using Test-Time Feedback
We propose a method for adapting neural networks to distribution shifts at test-time.
Task Discovery: Finding the Tasks that Neural Networks Generalize on
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?
3D Common Corruptions and Data Augmentation
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.
Robustness via Cross-Domain Ensembles
We present a method for making neural network predictions robust to shifts from the training data distribution.
Robustness via Probabilistic Cross-Task Ensembles
We present a method for making predictions using neural networks that, at the test time, is robust against shifts from the training data distribution.
Robust Learning Through Cross-Task Consistency
Visual perception entails solving a wide set of tasks, e. g., object detection, depth estimation, etc.
Iterative Classroom Teaching
We consider the machine teaching problem in a classroom-like setting wherein the teacher has to deliver the same examples to a diverse group of students.
