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Hybrid Deep Learning for Noisy Label Classification
An applied machine learning system for image classification under noisy real-world labels, using contrastive pretraining and robust learning strategies.
PyTorchComputer VisionContrastive LearningRobust Learning
Overview
This project explores building reliable image classification systems when training data contains noisy or imperfect labels — a common real-world challenge in applied machine learning.
What I Built
- ResNet-based image classification pipeline
- Contrastive pretraining workflow
- Robust training strategies for noisy labels
- Reproducible training and evaluation system
Key Engineering Decisions
- Used contrastive pretraining to stabilize representation learning
- Focused on robustness and generalization instead of benchmark chasing
- Designed experiments for reproducibility and structure
- Prioritized system reliability over raw accuracy metrics