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Found 9 papers, 4 papers with code
Generating High Fidelity Data from Low-density Regions using Diffusion Models
We observe that uniform sampling from diffusion models predominantly samples from high-density regions of the data manifold.
Fairness Indicators for Systematic Assessments of Visual Feature Extractors
Systematic diagnosis of fairness, harms, and biases of computer vision systems is an important step towards building socially responsible systems.
Towards Measuring Fairness in Speech Recognition: Casual Conversations Dataset Transcriptions
This paper presents initial Speech Recognition results on "Casual Conversations" -- a publicly released 846 hour corpus designed to help researchers evaluate their computer vision and audio models for accuracy across a diverse set of metadata, including age, gender, and skin tone.
Localized Uncertainty Attacks
Under this threat model, we create adversarial examples by perturbing only regions in the inputs where a classifier is uncertain.
Towards Measuring Fairness in AI: the Casual Conversations Dataset
The videos were recorded in multiple U. S. states with a diverse set of adults in various age, gender and apparent skin tone groups.
What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation?
The finding that very large networks can be trained efficiently and reliably has led to a paradigm shift in computer vision from engineered solutions to learning formulations.
Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems
While variational methods have been among the most powerful tools for solving linear inverse problems in imaging, deep (convolutional) neural networks have recently taken the lead in many challenging benchmarks.
Deep Depth From Focus
Depth from focus (DFF) is one of the classical ill-posed inverse problems in computer vision.
Image-based localization using LSTMs for structured feature correlation
In this work we propose a new CNN+LSTM architecture for camera pose regression for indoor and outdoor scenes.
