My GAN generates images but they look washed out.Many samples look almost identical.Training loss looks stable.But the visual quality never improves.
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My speech-to-text model produces accurate transcripts when tested in a quiet office.However, when I try to use it in public places, accuracy drops sharply.Background noise causes words to be skipped or misheard.The model feels fragile outside controlled ...
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 ...
I upgraded to a GPU with much more VRAM.I increased the batch size to use the available memory.Now the training is noticeably slower per epoch.There are no errors, but performance feels worse than before.
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.
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.
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 fine-tuned a Transformer model without any memory issues.But when I call model.generate(), CUDA runs out of memory.This happens even for short prompts.Training worked fine, so this feels confusing.
The training loss drops steadily during fine-tuning.But the translated sentences are grammatically wrong.BLEU and other quality metrics do not improve.It feels like the model is optimizing the wrong thing.