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.
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My model gives great accuracy on my laptop.When deployed on a server, predictions become inconsistent.The same input sometimes produces different outputs.Nothing crashes, but the behavior is unreliable.
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 am training a deep network for a regression task.The loss drops initially but then stops changing.Even after many epochs it never improves.The model is clearly underperforming.
The model classifies upright images correctly.Rotated versions of the same images fail.The content is the same.Only orientation changed.
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 ...
My model uses both image and text inputs.It works well when both are provided.If one modality is missing, outputs become random or broken.Real-world data is often incomplete.
The reconstruction loss is very low on training images.But when I test on new data, outputs look distorted.The model seems confident but wrong.It feels like it memorized the dataset.
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.
I trained a CNN to classify multiple object categories from images.The training completes without errors and the accuracy looks decent.But when I run inference, every image gets the same label.Even very different images are predicted as the ...