2.2k citations · 3.7k across the 24 of their papers we have counts for
6 papers · 2 filters
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…