The agent performs well in simulation.When deployed in the real world, it makes strange decisions.The physics is slightly different.Small changes lead to big failures.
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My diagnostic CNN shows high accuracy on data from one hospital.When tested on scans from a different hospital, performance drops drastically.The disease patterns are the same.Only the scanners and imaging pipelines differ.
I fine-tuned a pretrained Transformer on a small custom dataset.Training finishes without errors.But the generated outputs look random and off-topic.It feels like the model forgot everything.
The base model worked well before.After fine-tuning on new data, accuracy drops everywhere.Even old categories are misclassified.The model seems to have forgotten what it knew.
I trained a Keras model that gives good validation accuracy.After saving and loading it, the predictions become completely wrong.Even training samples are misclassified.Nothing crashes, but the outputs no longer make sense.