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Zahra Gharehmahmoodlee
Zahra Gharehmahmoodlee

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Self-Supervised Visual Representation Learning with SimCLR: A Practical Implementation

A hands-on exploration of contrastive learning on CIFAR-10

Abstract
This project implements SimCLR (Chen et al., 2020), a state-of-the-art self-supervised learning framework, to learn meaningful visual representations from unlabeled CIFAR-10 images. By leveraging contrastive learning and simple data augmentations, we achieve 82.4% linear evaluation accuracy – demonstrating the power of label-free representation learning for computer vision tasks.

- 1. Introduction to Self-Supervised Learning
The Label Efficiency Problem
Traditional supervised learning requires expensive labeled datasets. Self-supervised learning (SSL) circumvents this by creating supervisory signals directly from the data's structure.
Why SimCLR?
SimCLR's simplicity and effectiveness make it ideal for:

  • Pretraining visual models with limited labels
  • Studying fundamental representation learning
  • Developing SSL research skills (highly relevant at UVic's computer vision labs) 2. Methodology Key Components 
_**Component**_         **_Implementation Details_**
Base Encoder            ResNet-50 (modified)
Projection Head         2-layer MLP (2048→512→128)
Augmentation Policy Random crop, flip, color jitter, blur
Contrastive Loss    NT-Xent (τ=0.5)
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Technical Workflow
Input Pipeline:
Generate two augmented views per image

transform = Compose([
RandomResizedCrop(32),
RandomHorizontalFlip(),
ColorJitter(0.8, 0.8, 0.8, 0.2)
])

Training:
Maximize similarity between positive pairs
Minimize similarity across negatives

Evaluation:
Linear probing on frozen features
3. Results & Analysis
Performance Metrics

Epochs  Batch Size  Top-1 Accuracy
100   256             82.4%
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4. Practical Applications
This implementation demonstrates how SSL can benefit:

  • Medical Imaging: Learn from unlabeled scans
  • Remote Sensing: Pretrain on satellite imagery
  • Robotics: Develop visual priors with minimal supervision

5. Getting Started
Implementation Guide :

# Clone repository
git clone https://github.com/zahramh99/simclr-cifar10.git
cd simclr-cifar10

# Install dependencies
pip install -r requirements.txt

# Train model
python train.py
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Custom Dataset Adaptation

  • Replace images in ./data/custom/
  • Modify config.py: 
dataset = "custom"
image_size = 32  # Match CIFAR-10 dimensions
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6. Conclusion
This project verifies that SimCLR can learn high-quality representations without labels, achieving competitive performance on CIFAR-10. The modular PyTorch implementation serves as:

  • A practical SSL tutorial
  • A baseline for future research
  • A template for industrial applications

Future Work: Extend to DINOv2 or investigate hybrid supervised/self-supervised approaches.

References

  • Chen et al. (2020). A Simple Framework for Contrastive Learning. arXiv:2002.05709

Author: Zahra Gharehmahmoodlee
GitHub: github.com/zahramh99/simclr-cifar10

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