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Found 2 papers, 1 papers with code
Lost in Translation: Modern Neural Networks Still Struggle With Small Realistic Image Transformations
Our findings reveal that a mere one-pixel translation can result in a significant change in the predicted image representation for approximately 40% of the test images in state-of-the-art models (e. g. open-CLIP trained on LAION-2B or DINO-v2) , while models that are explicitly constructed to be robust to cyclic translations can still be fooled with 1 pixel realistic (non-cyclic) translations 11% of the time.
Some Grammatical Errors are Frequent, Others are Important
In Grammatical Error Correction, systems are evaluated by the number of errors they correct.
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