Summary of Contrastive Learning to Fine-tune Feature Extraction Models For the Visual Cortex, by Alex Mulrooney and Austin J. Brockmeier
Contrastive Learning to Fine-Tune Feature Extraction Models for the Visual Cortex
by Alex Mulrooney, Austin J. Brockmeier
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A machine learning-based approach is presented to predict neural responses in the visual cortex by optimizing feature extraction from natural images. A convolutional neural network is fine-tuned using contrastive learning (CL) to maximize information sharing between image features and neural responses across voxels in a region of interest (ROI). The Natural Scenes Dataset, containing high-resolution fMRI responses to tens of thousands of images, is exploited for training. The CL-fine-tuned models show improved encoding accuracy in early visual ROIs compared to the pretrained network and baseline approaches. Inter-subject transfer and pooling subjects for fine-tuning are also investigated. Additionally, the performance of the fine-tuned models on image classification tasks and ROI-specific dimensionality reduction are examined. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses a special type of machine learning called contrastive learning (CL) to better understand how our brains process visual information from natural images. It’s like training a computer to recognize what makes a picture look interesting or important. The researchers used a big dataset of brain scans and image data to teach the computer to match features in the images with the way different parts of the brain respond to them. They found that this approach worked better than just using a regular machine learning model, especially when it came to understanding how different people’s brains process visual information. |
Keywords
» Artificial intelligence » Dimensionality reduction » Feature extraction » Fine tuning » Image classification » Machine learning » Neural network
