17 citations · 41 across the 14 of their papers we have counts for
5 papers · 1 filter
Fairness via Adversarial Attribute Neighbourhood Robust Learning
Qi Qi, Shervin Ardeshir, Yi Xu +1
Improving fairness between privileged and less-privileged sensitive attribute groups (e.g, {race, gender}) has attracted lots of attention. To enhance the model performs uniformly…
Uncertainty in Contrastive Learning: On the Predictability of Downstream Performance
Shervin Ardeshir, Navid Azizan
The superior performance of some of today's state-of-the-art deep learning models is to some extent owed to extensive (self-)supervised contrastive pretraining on large-scale datas…
On Negative Sampling for Audio-Visual Contrastive Learning from Movies
Mahdi M. Kalayeh, Shervin Ardeshir, Lingyi Liu +2
The abundance and ease of utilizing sound, along with the fact that auditory clues reveal a plethora of information about what happens in a scene, make the audio-visual space an in…
Character-focused Video Thumbnail Retrieval
Shervin Ardeshir, Nagendra Kamath, Hossein Taghavi
We explore retrieving character-focused video frames as candidates for being video thumbnails. To evaluate each frame of the video based on the character(s) present in it, characte…
Estimating Structural Disparities for Face Models
Shervin Ardeshir, Cristina Segalin, Nathan Kallus
In machine learning, disparity metrics are often defined by measuring the difference in the performance or outcome of a model, across different sub-populations (groups) of datapoin…