Implementation of a self-supervised learning model trained with contrastive and reconstruction losses on the CIFAR-10 dataset.

This repository contains the code for a self-supervised learning model trained with contrastive and reconstruction losses. The model is implemented using the CIFAR-10 dataset and an encoder-decoder architecture.

• Objective: Implement a self-supervised learning approach using contrastive and reconstruction losses.
• Data: CIFAR-10 dataset
• Model Architecture: Encoder-decoder with contrastive and reconstruction losses
• Training Procedure: Simultaneous training with pairs of images
• Evaluation: Analysis of learned representations and effectiveness of contrastive and reconstruction losses
• Future Steps: Further experiments, application of learned representations for downstream tasks

• selfsupervised_contrastive_reconstruction.ipynb: Jupyter Notebook containing the project code
• README.md: This file providing an overview of the project

1. Clone the repository:

git clone https://github.com/chibbss/self-supervised-learning-contrastive-reconstruction.git