Generalization Bounds for Deep Transfer Learning Using Majority Predictor Accuracy

Cuong N. Nguyen; Lam Si Tung Ho; Vu Dinh; Tal Hassner; Cuong V. Nguyen

Generalization Bounds for Deep Transfer Learning Using Majority Predictor Accuracy

Cuong N. Nguyen, Lam Si Tung Ho, Vu Dinh, Tal Hassner, Cuong V. Nguyen

نتاج البحث: فصل من :كتاب / تقرير / مؤتمر › منشور من مؤتمر › مراجعة النظراء

ملخص

We analyze new generalization bounds for deep learning models trained by transfer learning from a source to a target task. Our bounds utilize a quantity called the majority predictor accuracy, which can be computed efficiently from data. We show that our theory is useful in practice since it implies that the majority predictor accuracy can be used as a transferability measure, a fact that is also validated by our experiments.

اللغة الأصلية	الإنجليزيّة
عنوان منشور المضيف	Proceedings of 2022 International Symposium on Information Theory and Its Applications, ISITA 2022
ناشر	Institute of Electrical and Electronics Engineers Inc.
الصفحات	139-143
عدد الصفحات	5
رقم المعيار الدولي للكتب (الإلكتروني)	9784885523410
حالة النشر	نُشِر - 2022
منشور خارجيًا	نعم
الحدث	17th International Symposium on Information Theory and Its Applications, ISITA 2022 - Tsukuba, Ibaraki, اليابان المدة: ١٧ أكتوبر ٢٠٢٢ → ١٩ أكتوبر ٢٠٢٢

سلسلة المنشورات

الاسم	Proceedings of 2022 International Symposium on Information Theory and Its Applications, ISITA 2022

!!Conference

!!Conference	17th International Symposium on Information Theory and Its Applications, ISITA 2022
الدولة/الإقليم	اليابان
المدينة	Tsukuba, Ibaraki
المدة	١٧/١٠/٢٢ → ١٩/١٠/٢٢

ملاحظة ببليوغرافية

Publisher Copyright:
© 2022 IEICE.

الملفات والروابط الأخرى

Link to publication in Scopus

قم بذكر هذا

Nguyen, C. N., Ho, L. S. T., Dinh, V., Hassner, T., & Nguyen, C. V. (2022). Generalization Bounds for Deep Transfer Learning Using Majority Predictor Accuracy. في Proceedings of 2022 International Symposium on Information Theory and Its Applications, ISITA 2022 (الصفحات 139-143). (Proceedings of 2022 International Symposium on Information Theory and Its Applications, ISITA 2022). Institute of Electrical and Electronics Engineers Inc..

Nguyen, Cuong N. ; Ho, Lam Si Tung ; Dinh, Vu وآخرون. / Generalization Bounds for Deep Transfer Learning Using Majority Predictor Accuracy. Proceedings of 2022 International Symposium on Information Theory and Its Applications, ISITA 2022. Institute of Electrical and Electronics Engineers Inc., 2022. الصفحات 139-143 (Proceedings of 2022 International Symposium on Information Theory and Its Applications, ISITA 2022).

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title = "Generalization Bounds for Deep Transfer Learning Using Majority Predictor Accuracy",

abstract = "We analyze new generalization bounds for deep learning models trained by transfer learning from a source to a target task. Our bounds utilize a quantity called the majority predictor accuracy, which can be computed efficiently from data. We show that our theory is useful in practice since it implies that the majority predictor accuracy can be used as a transferability measure, a fact that is also validated by our experiments.",

author = "Nguyen, {Cuong N.} and Ho, {Lam Si Tung} and Vu Dinh and Tal Hassner and Nguyen, {Cuong V.}",

note = "Publisher Copyright: {\textcopyright} 2022 IEICE.; 17th International Symposium on Information Theory and Its Applications, ISITA 2022 ; Conference date: 17-10-2022 Through 19-10-2022",

year = "2022",

language = "אנגלית",

series = "Proceedings of 2022 International Symposium on Information Theory and Its Applications, ISITA 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Nguyen, CN, Ho, LST, Dinh, V, Hassner, T & Nguyen, CV 2022, Generalization Bounds for Deep Transfer Learning Using Majority Predictor Accuracy. في Proceedings of 2022 International Symposium on Information Theory and Its Applications, ISITA 2022. Proceedings of 2022 International Symposium on Information Theory and Its Applications, ISITA 2022, Institute of Electrical and Electronics Engineers Inc., الصفحات 139-143, 17th International Symposium on Information Theory and Its Applications, ISITA 2022, Tsukuba, Ibaraki, اليابان, ١٧/١٠/٢٢.

Generalization Bounds for Deep Transfer Learning Using Majority Predictor Accuracy. / Nguyen, Cuong N.; Ho, Lam Si Tung; Dinh, Vu وآخرون.
Proceedings of 2022 International Symposium on Information Theory and Its Applications, ISITA 2022. Institute of Electrical and Electronics Engineers Inc., 2022. صفحة 139-143 (Proceedings of 2022 International Symposium on Information Theory and Its Applications, ISITA 2022).

نتاج البحث: فصل من :كتاب / تقرير / مؤتمر › منشور من مؤتمر › مراجعة النظراء

TY - GEN

T1 - Generalization Bounds for Deep Transfer Learning Using Majority Predictor Accuracy

AU - Nguyen, Cuong N.

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AU - Dinh, Vu

AU - Hassner, Tal

AU - Nguyen, Cuong V.

PY - 2022

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N2 - We analyze new generalization bounds for deep learning models trained by transfer learning from a source to a target task. Our bounds utilize a quantity called the majority predictor accuracy, which can be computed efficiently from data. We show that our theory is useful in practice since it implies that the majority predictor accuracy can be used as a transferability measure, a fact that is also validated by our experiments.

AB - We analyze new generalization bounds for deep learning models trained by transfer learning from a source to a target task. Our bounds utilize a quantity called the majority predictor accuracy, which can be computed efficiently from data. We show that our theory is useful in practice since it implies that the majority predictor accuracy can be used as a transferability measure, a fact that is also validated by our experiments.

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T3 - Proceedings of 2022 International Symposium on Information Theory and Its Applications, ISITA 2022

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BT - Proceedings of 2022 International Symposium on Information Theory and Its Applications, ISITA 2022

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 17th International Symposium on Information Theory and Its Applications, ISITA 2022

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ER -

Nguyen CN, Ho LST, Dinh V, Hassner T, Nguyen CV. Generalization Bounds for Deep Transfer Learning Using Majority Predictor Accuracy. في Proceedings of 2022 International Symposium on Information Theory and Its Applications, ISITA 2022. Institute of Electrical and Electronics Engineers Inc. 2022. صفحة 139-143. (Proceedings of 2022 International Symposium on Information Theory and Its Applications, ISITA 2022).

Generalization Bounds for Deep Transfer Learning Using Majority Predictor Accuracy

ملخص

سلسلة المنشورات

!!Conference

ملاحظة ببليوغرافية

الملفات والروابط الأخرى

بصمة

قم بذكر هذا