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Vikram Ramaswamy FPO

Date and Time
Monday, May 8, 2023 - 12:00pm to 2:00pm
Computer Science 402

Vikram Ramaswamy will present his FPO "Tackling bias within Computer Vision Models" on Monday, May 8, 2023 at 12:00 PM in CS 402.

Location: CS 402

The members of Vikram’s committee are as follows: Examiners: Olga Russakovsky (Adviser), Ellen Zhong, Ryan Adams
Readers: Jia Deng, Andrés Monroy-Hernández

A copy of his thesis is available upon request.  Please email if you would like a copy of the thesis.

Everyone is invited to attend his talk.

Abstract follows below:
Over the past decade the rapid increase in the ability of computer vision models has led to their applications in a variety of real-world applications from self-driving cars to medical diagnoses. However, there is increasing concern about the fairness and transparency of these models. In this thesis, we tackle these issue of bias within these models along two different axes. First, we consider the datasets that these models are trained on. We use two different methods to create a more balanced training dataset. First, we create a synthetic balanced dataset by sampling strategically from the latent space of a generative network. Next, we explore the potential of creating a dataset through a method other than scraping the internet: we solicit images from workers around the world, creating a dataset that is balanced across different geographical regions. Both techniques are shown to help create models with less bias. Second, we consider methods to improve interpretability of these models, which can then reveal potential biases within the model. We investigate a class of interpretability methods called concept-based methods that output explanations for models in terms of human understandable semantic concepts. We demonstrate the need for more careful development of the datasets used to learn the explanation as well as the concepts used within these explanations. We construct a new method that allows for users to select a trade-off between the understandability and faithfulness of the explanation. Finally, we discuss how methods that completely explain a model can be developed, and provide heuristics for the same.

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