Tag: Contrastive Learning
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SimCLR vs CLIP: Why Contrastive Learning Failed in Prod
SimCLR hit 89% accuracy but burned $50K in GPU costs. CLIP served 2000 QPS at 45ms. Real latency benchmarks and the trade-offs papers don't mention.
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SimCLR for Bearing Fault Classification with Few Labels
SimCLR pretraining hits 89.3% accuracy with only 5 labeled bearing fault samples. Step-by-step contrastive learning on CWRU dataset.
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CLIP ViT-L/14 Zero-Shot ImageNet Accuracy: 76.2% Without Fine-Tuning
CLIP ViT-L/14@336px hits 76.2% zero-shot ImageNet top-1 accuracy. We break down how contrastive pretraining works and where it fails.
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Self-Supervised Contrastive Learning for Fault Diagnosis
SimCLR contrastive pretraining boosted fault diagnosis from 73% to 89% using 8,000 unlabeled vibration signals. Here's the exact PHM workflow.