> ML_ALGORITHM // CONTRASTIVE-REPRESENTATION-LEARNING-SIMCLR_v1.0
Simple Framework for Contrastive Learning (SimCLR)
Contrastive self-supervised visual representation framework maximizing agreement between differently augmented views of the same image via InfoNCE loss.
Contrastive Representation Learningself-supervisedblack-boxlarge (>100k)
Back to All AlgorithmsComputational Complexity
Training Complexity:O(epochs * batch_size^2 * forward_pass)
Inference Complexity:O(forward_pass)
Hardware Profile
CPU Friendly:No
Requires GPU:Yes
Memory Footprint:high
Interpretability & Data
Interpretability Tier:black-box
Training Data Needs:large (>100k)
Interpretability Assessment
Learns a metric embedding space where cosine distance reflects semantic invariant similarity.
Suitable Tasks & Supported Modalities
Suitable Tasks:
feature extractionimage classification
Supported Modalities:
image
Implementing Libraries
Foundational Literature
A Simple Framework for Contrastive Learning of Visual Representations (SimCLR)Ting Chen, Simon Kornblith (2020) · International Conference on Machine Learning (ICML)
Common Pitfalls & Warnings
- Training with small batch sizes (<1024) leads to severe degradation due to insufficient negative samples
- Color histogram shortcuts if random color jittering is omitted