The model produces grammatically correct text.But it keeps repeating the same phrases.The output never moves forward.It feels stuck in a loop?
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My CNN reaches over 95% accuracy on the training set.But on the test set it drops below 40%.The data comes from the same source.I feel the model is memorizing instead of learning.
I added thousands of new user interactions to my training dataset.Instead of improving, the recommendation quality dropped.Users are now getting irrelevant suggestions.It feels like more data made the model less accurate.
My RNN works fine on short sequences.When I give it longer inputs, predictions become random.Loss increases with sequence length.It feels like the model forgets earlier information.
The system performs well in offline tests.Under real user traffic, errors appear.Latency increases and predictions degrade.The same model is running.
My image classifier performs very well on bright daylight photos.When images are darker or taken indoors, accuracy drops sharply.The objects are still the same.Only the lighting seems different.
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 trained an object detection model on a mixed dataset containing people, vehicles, and small objects like phones and traffic signs.The model detects large objects such as cars and people very reliably.However, it almost completely ignores smaller objects, ...
I am training a convolutional neural network on a custom image dataset using PyTorch.For the first few batches the loss looks normal, but suddenly it becomes NaN and never recovers.There are no crashes or stack traces, only the ...