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ઌߦݚڀ nଟஈύΠϓϥΠϯ[e.g.1,2]΍ϩόετͳଛࣦؔ਺[e.g.3,4] Λ༻͍ͯαϯϓϧΛࣝผ͢Δํ๏͕ݚڀ͞Ε͍ͯΔ [1] P. Chen, B. Liao, G. Chen, and S. Zhang. Understanding and utilizing deep neural networks trained with noisy labels. In ICML, 2019. [2] J. Han, P. Luo, and X. Wang. Deep self-learning from noisy labels. In CVPR, 2019. [3] Y. Xu, P. Cao, Y. Kong, and Y. Wang. LDMI: An information-theoretic noise-robust loss function. In NeurIPS, 2019. [4] Z. Zhang and M. R. Sabuncu. Generalized cross entropy loss for training deep neural networks with noisy labels. In NeurIPS, 2018

ఏҊख๏ɿ n"SFB6OEFSUIF.BSHJOʀ"6. l αϯϓϧͷׂΓ౰ͯΫϥεͱϩδοτ஋͕࠷΋ߴ͍ඇׂΓ౰ͯΫϥε ͷϩδοτ஋ؒͷࠩΛଌఆ͢Δ l ֤ΤϙοΫʹ͓͍ͯଌఆ͞ΕͨαϯϓϧͷϚʔδϯΛฏۉԽ͢Δ͜ͱ Ͱଊ͑Δ T：エポックの総数

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ఏҊख๏ɿ nݕূσʔλͳ͠Ͱͷᮢ஋Λֶश͢Δܭࢉޮ཰ͷྑ͍ઓུΛఏҊɽ l ᮢ஋͸σʔληοτʹґଘ͢Δ nᮢ஋αϯϓϧʹΑΔޡϥϕϧͷഉআ l ֶशதʹᮢ஋αϯϓϧʢِͷσʔλʣΛૠೖ͠ɺϥϕϧ෇͚͞Εͨ σʔλʹΑΔֶशμΠφϛΫεΛ໛฿͢Δɽ l ᮢ஋αϯϓϧͱಉ౳͔ͦΕҎ্ͷ"6.Λ࣋ͭσʔλ͸ɺޡϥϕϦ ϯά͞ΕͨͱԾఆ͢Δ͜ͱ͕Ͱ͖Δɽ

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ఏҊख๏ɿ nϓϩηεɿ ᮢ஋αϯϓϧͷαϒηοτD!"# Λ࡞੒͢Δ ᮢ஋αϯϓϧΛؚΉमਖ਼ֶशηοτD′$%&'( Λ࡞੒͢Δ 3. D′$%&'( ͰωοτϫʔΫΛ࠷ॳʹֶश཰͕Լ͕Δ·Ͱֶश˞ͤ͞ɼશ σʔλͷ"6.Λଌఆ͢Δɽ ˋᮢ஋αϯϓϧ"6.ʢЋʣΛܭࢉ͢Δɽ ЋΛᮢ஋ͱͯ͠ɺϥϕϧ෇͚͞ΕͨσʔλΛࣝผ͢Δ ※ 学習率が低下する前に学習を停⽌することで、ネットワークが収束し、その結果、誤ラベルサンプルを記憶してしまうことを防ぐ

݁Ռɿ nൈਮ

ॴײ nϥΠϒϥϦެ։͞Ε͍ͯΔͷͰ࢖͍΍͍͢ l IUUQTHJUIVCDPNBTBQQSFTFBSDIBVN nࣗ࡞σʔληοτͷਫ਼౓ఈ্͛Ͱ࢖͑ͦ͏ nϞσϧʹͱͬͯͷ”ֶश͠΍͍͢σʔλ”ͱ”ֶश͠ʹ͍͘ σʔλ”͕Θ͔ΔͷͰɼσʔλαϯϓϦϯάʹ΋࢖͑ͦ͏