2.2k citations · 3.7k across the 24 of their papers we have counts for
21 papers · 1 filter
GELDA: A generative language annotation framework to reveal visual biases in datasets
Krish Kabra, Kathleen M. Lewis, Guha Balakrishnan
Bias analysis is a crucial step in the process of creating fair datasets for training and evaluating computer vision models. The bottleneck in dataset analysis is annotation, which…
Benchmarking Algorithmic Bias in Face Recognition: An Experimental Approach Using Synthetic Faces and Human Evaluation
Hao Liang, Pietro Perona, Guha Balakrishnan
We propose an experimental method for measuring bias in face recognition systems. Existing methods to measure bias depend on benchmark datasets that are collected in the wild and a…
F?D: On understanding the role of deep feature spaces on face generation evaluation
Krish Kabra, Guha Balakrishnan
Perceptual metrics, like the Fréchet Inception Distance (FID), are widely used to assess the similarity between synthetically generated and ground truth (real) images. The key idea…
SplineCam: Exact Visualization and Characterization of Deep Network Geometry and Decision Boundaries
Ahmed Imtiaz Humayun, Randall Balestriero, Guha Balakrishnan +1
Current Deep Network (DN) visualization and interpretability methods rely heavily on data space visualizations such as scoring which dimensions of the data are responsible for thei…
Linking convolutional kernel size to generalization bias in face analysis CNNs
Hao Liang, Josue Ortega Caro, Vikram Maheshri +2
Training dataset biases are by far the most scrutinized factors when explaining algorithmic biases of neural networks. In contrast, hyperparameters related to the neural network ar…
WIRE: Wavelet Implicit Neural Representations
Vishwanath Saragadam, Daniel LeJeune, Jasper Tan +3
Implicit neural representations (INRs) have recently advanced numerous vision-related areas. INR performance depends strongly on the choice of the nonlinear activation function emp…
Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers
Dominik Zietlow, Michael Lohaus, Guha Balakrishnan +4
Algorithmic fairness is frequently motivated in terms of a trade-off in which overall performance is decreased so as to improve performance on disadvantaged groups where the algori…
MINER: Multiscale Implicit Neural Representations
Vishwanath Saragadam, Jasper Tan, Guha Balakrishnan +2
We introduce a new neural signal model designed for efficient high-resolution representation of large-scale signals. The key innovation in our multiscale implicit neural representa…
Rayleigh EigenDirections (REDs): GAN latent space traversals for multidimensional features
Guha Balakrishnan, Raghudeep Gadde, Aleix Martinez +1
We present a method for finding paths in a deep generative model's latent space that can maximally vary one set of image features while holding others constant. Crucially, unlike p…
Matched sample selection with GANs for mitigating attribute confounding
Chandan Singh, Guha Balakrishnan, Pietro Perona
Measuring biases of vision systems with respect to protected attributes like gender and age is critical as these systems gain widespread use in society. However, significant correl…
Better Aggregation in Test-Time Augmentation
Divya Shanmugam, Davis Blalock, Guha Balakrishnan +1
Test-time augmentation -- the aggregation of predictions across transformed versions of a test input -- is a common practice in image classification. Traditionally, predictions are…
Towards causal benchmarking of bias in face analysis algorithms
Guha Balakrishnan, Yuanjun Xiong, Wei Xia +1
Measuring algorithmic bias is crucial both to assess algorithmic fairness, and to guide the improvement of algorithms. Current methods to measure algorithmic bias in computer visio…
Visual Deprojection: Probabilistic Recovery of Collapsed Dimensions
Guha Balakrishnan, Adrian V. Dalca, Amy Zhao +3
We introduce visual deprojection: the task of recovering an image or video that has been collapsed along a dimension. Projections arise in various contexts, such as long-exposure p…
Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces
Adrian V. Dalca, Guha Balakrishnan, John Guttag +1
Classical deformable registration techniques achieve impressive results and offer a rigorous theoretical treatment, but are computationally intensive since they solve an optimizati…
Data augmentation using learned transformations for one-shot medical image segmentation
Amy Zhao, Guha Balakrishnan, Frédo Durand +2
Image segmentation is an important task in many medical applications. Methods based on convolutional neural networks attain state-of-the-art accuracy; however, they typically rely…
Fast Learning-based Registration of Sparse 3D Clinical Images
Kathleen M. Lewis, Natalia S. Rost, John Guttag +1
We introduce SparseVM, a method that registers clinical-quality 3D MR scans both faster and more accurately than previously possible. Deformable alignment, or registration, of clin…
VoxelMorph: A Learning Framework for Deformable Medical Image Registration
Guha Balakrishnan, Amy Zhao, Mert R. Sabuncu +2
We present VoxelMorph, a fast learning-based framework for deformable, pairwise medical image registration. Traditional registration methods optimize an objective function for each…
Unsupervised Learning for Fast Probabilistic Diffeomorphic Registration
Adrian V. Dalca, Guha Balakrishnan, John Guttag +1
Traditional deformable registration techniques achieve impressive results and offer a rigorous theoretical treatment, but are computationally intensive since they solve an optimiza…
Synthesizing Images of Humans in Unseen Poses
Guha Balakrishnan, Amy Zhao, Adrian V. Dalca +2
We address the computational problem of novel human pose synthesis. Given an image of a person and a desired pose, we produce a depiction of that person in that pose, retaining the…
An Unsupervised Learning Model for Deformable Medical Image Registration
Guha Balakrishnan, Amy Zhao, Mert R. Sabuncu +2
We present a fast learning-based algorithm for deformable, pairwise 3D medical image registration. Current registration methods optimize an objective function independently for eac…
A Video-Based Method for Objectively Rating Ataxia
Ronnachai Jaroensri, Amy Zhao, Guha Balakrishnan +4
For many movement disorders, such as Parkinson's disease and ataxia, disease progression is visually assessed by a clinician using a numerical disease rating scale. These tests are…