The Algonauts Project 2023 Challenge: UARK-UAlbany Team Solution
This work addresses a domain-specific problem in computational neuroscience for predicting brain activity from visual stimuli, but it appears incremental as it builds on existing challenge frameworks without claiming major breakthroughs.
The authors tackled the Algonauts Project 2023 Challenge by developing an image-based brain encoder to predict brain responses to natural visual scenes, using a two-step training process with ensemble methods, but no concrete performance numbers are provided in the abstract.
This work presents our solutions to the Algonauts Project 2023 Challenge. The primary objective of the challenge revolves around employing computational models to anticipate brain responses captured during participants' observation of intricate natural visual scenes. The goal is to predict brain responses across the entire visual brain, as it is the region where the most reliable responses to images have been observed. We constructed an image-based brain encoder through a two-step training process to tackle this challenge. Initially, we created a pretrained encoder using data from all subjects. Next, we proceeded to fine-tune individual subjects. Each step employed different training strategies, such as different loss functions and objectives, to introduce diversity. Ultimately, our solution constitutes an ensemble of multiple unique encoders. The code is available at https://github.com/uark-cviu/Algonauts2023