My GAN generates faces.But many look distorted or unnatural.Eyes and mouths appear in wrong positions.The training seems stable, but outputs are flawed.
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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 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.
The system performs well in offline tests.Under real user traffic, errors appear.Latency increases and predictions degrade.The same model is running.
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.
My model recognizes actions well in static camera videos.When the camera pans or shakes, predictions become unstable.The action is the same.Only the camera motion changes.
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, ...
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.
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.
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 ...