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Register a new userPosterior Differential Regularization with f-divergence for Improving Model Robustness
التنظيم التفاضلي الخلفي مع اختلاف F لتحسين النموذج
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نحن نبذة عن مشكلة تعزيز متانة النموذج من خلال التنظيم. على وجه التحديد، نركز على الأساليب التي تنظم الفرق الخلفي النموذجي بين المدخلات النظيفة والصاخبة. من الناحية النظرية، نحن نقدم اتصالا بطريقتين حديثين، وانتظام جاكوبي والتدريب الخصم الافتراضي، في إطار هذا الإطار. بالإضافة إلى ذلك، نعيد بتعميم التنظيم التفاضلي الخلفي لعائلة الاختلافات F وتوصيف الإطار العام من حيث مصفوفة الجاكوبيان. تجريبيا، قارنا هذه التحسينات وتدريب بيرت القياسي على مجموعة متنوعة من المهام لتوفير ملف شامل لتأثيرها على تعميم النموذج. لكلا الإعدادات ذات الإشراف بالكامل وشبه الإشراف، نوضح أن تنظيم الفرق الخلفي الذي يمكن أن يؤدي إلى اختلاف F إلى متانة نموذج جيد. على وجه الخصوص، مع اختلاف F-Supplgence مناسب، يمكن أن يحقق نموذج BERT-BASE تعميما قابلا للمقارنة كظرفي كبير لسيناريوهات التحول في المجال والمواد والمناطق، مما يشير إلى إمكانات كبيرة للإطار المقترح لتعزيز متانة نموذج NLP.
We address the problem of enhancing model robustness through regularization. Specifically, we focus on methods that regularize the model posterior difference between clean and noisy inputs. Theoretically, we provide a connection of two recent methods, Jacobian Regularization and Virtual Adversarial Training, under this framework. Additionally, we generalize the posterior differential regularization to the family of f-divergences and characterize the overall framework in terms of the Jacobian matrix. Empirically, we compare those regularizations and standard BERT training on a diverse set of tasks to provide a comprehensive profile of their effect on model generalization. For both fully supervised and semi-supervised settings, we show that regularizing the posterior difference with f-divergence can result in well-improved model robustness. In particular, with a proper f-divergence, a BERT-base model can achieve comparable generalization as its BERT-large counterpart for in-domain, adversarial and domain shift scenarios, indicating the great potential of the proposed framework for enhancing NLP model robustness.
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