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![大規模な画像の分類といえば
Deepで深イイ深層学習の独壇場
[Krizhevsky+, NIPS 2013]
[Mahendran+Vedaldi, CVPR 2015]](https://image.slidesharecdn.com/20150530kantocvcurriculumlearning-150530020447-lva1-app6892/75/Curriculum-Learning-CV-3-2048.jpg?cb=1725966678)
![2012年～：猫も杓子も Deep Learning 祭り
[Tomohiro Mito, Deep learning, slideshare, 2013]](https://image.slidesharecdn.com/20150530kantocvcurriculumlearning-150530020447-lva1-app6892/75/Curriculum-Learning-CV-5-2048.jpg?cb=1725966678)
![2012年～：猫も杓子も Deep Learning 祭り
[Tomohiro Mito, Deep learning, slideshare, 2013]
ISIってなんだよどこのチームだよ
同webサイトをみると･･･
ぼくら
Deep勢](https://image.slidesharecdn.com/20150530kantocvcurriculumlearning-150530020447-lva1-app6892/75/Curriculum-Learning-CV-6-2048.jpg?cb=1725966678)
![人間の学習では
• 最初は簡単な問題を解く。
• 時の経過とともに難しい問題も学ぶ。
計算機も見習うべきでは?!
Curriculum Learning
[Bengio+, ICML 2009]](https://image.slidesharecdn.com/20150530kantocvcurriculumlearning-150530020447-lva1-app6892/75/Curriculum-Learning-CV-16-2048.jpg?cb=1725966678)
![カリキュラムってどうやって定義するの？
先程の例はやたらハンドメイドだったけど･･･？
→実際、お手製のカリキュラムも多い
• [Basu+Christensen, AAAI 2013]
– 機械の識別面を人に教示する手法
– カリキュラム＝識...](https://image.slidesharecdn.com/20150530kantocvcurriculumlearning-150530020447-lva1-app6892/75/Curriculum-Learning-CV-23-2048.jpg?cb=1725966678)
![Self-Paced Learning [Kumar+, NIPS 2010]
カリキュラム𝒗：ある年齢𝜆で𝑖番目のサンプル(𝒙𝑖, 𝑦𝑖)を
学習するかどうかを示す0/1の値
𝒗 ∈ 0,1 𝑛
カリキュラム𝒗の下での学習：
𝒗によって選択さ...](https://image.slidesharecdn.com/20150530kantocvcurriculumlearning-150530020447-lva1-app6892/75/Curriculum-Learning-CV-25-2048.jpg?cb=1725966678)
![SPL with Diversity [Jiang+, NIPS 2014]
簡単かどうかだけじゃなくて、
Diverseな(色々な)タイプのサンプルを学習すべき。
動画認識コンペTRECVID内の「ロッククライミング」データによる例](https://image.slidesharecdn.com/20150530kantocvcurriculumlearning-150530020447-lva1-app6892/75/Curriculum-Learning-CV-30-2048.jpg?cb=1725966678)
![Curriculum Learning 適用例：SPLD原著
• 動画認識問題に適用
– Hollywood2、Olympic Sports データセット
[Wang+, ICCV 2013]と特徴量は同じ。
機械学習手法のSVMも同じ。
SP...](https://image.slidesharecdn.com/20150530kantocvcurriculumlearning-150530020447-lva1-app6892/75/Curriculum-Learning-CV-38-2048.jpg?cb=1725966678)
![Curriculum Learning 適用例：再掲
先程のスライドの再掲。
• [Basu+Christensen, AAAI 2013]
– 機械の識別面を人に教示する手法
– カリキュラム＝識別面からの距離
• [Spitkovsky+,...](https://image.slidesharecdn.com/20150530kantocvcurriculumlearning-150530020447-lva1-app6892/75/Curriculum-Learning-CV-39-2048.jpg?cb=1725966678)
![Curriculum Learning 適用例：マルチメディア
• [Xiao+, ACM MM 2014]
– Deep Learning による画
像認識
– データが増えた際の新
規クラス学習に着目
• [Jiang+, ACM MM 2...](https://image.slidesharecdn.com/20150530kantocvcurriculumlearning-150530020447-lva1-app6892/75/Curriculum-Learning-CV-40-2048.jpg?cb=1725966678)
![各種機械学習手法への適用例
• 識別面の教示 [Basu+Christensen, AAAI 2013]
線形分類
• 構文解析 [Spitkovsky+, NIPS 2009]
EMアルゴリズム
• 物体検出やシーン認識 [Lapedriza...](https://image.slidesharecdn.com/20150530kantocvcurriculumlearning-150530020447-lva1-app6892/75/Curriculum-Learning-CV-41-2048.jpg?cb=1725966678)
