# From statistics to ai
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Impressed Structure In Deep Learning Multi-task Learning Domain adaptation Transfer learning GAN AnoGAN, Anomaly GAN Reinforcement learning Capsule, Spiking NN Black-box In Deep Learning LIME, Local interpretable model-agnostic explanations LRP, Layer-wise relevance propagation Advantage of Statistics In Deep Learning
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# From statistics to ai
- 2. 自我介紹 學經歷 緯創資通 高級工程師 (2015/3 ~2019/4) 長庚醫院 研究助理 (2013/9 ~2014/11) 中山大學 應數系統計碩士 (2011/9 ~ 2013/7) 高雄師大 數學系學士 (2005/9 ~ 2009/7) 獲獎 2017 台南智慧黑客松智慧醫療組第二名 2012 中山高大統計新秀 特殊經歷 訪問 MIT 的工程師 (2017/10 ~ 2018/10) 2 Terence Huang Terence0408 (Slideshare)
- 3. Outline • Impressed Structure In Deep Learning - Transfer learning - GAN - Reinforcement learning - Capsule, Spiking NN • Black-box • Advantage of Statistics 3
- 4. Outline • Impressed Structure In Deep Learning - Transfer learning - GAN - Reinforcement learning - Capsule, Spiking NN • Black-box • Advantage of Statistics 4
- 5. Impressed structure in Deep Learning • Transfer learning family 5 Same Task on Source and Target Domains Same Source and Target Marginal Distribution on X Same Task on Source and Target Domains “Usual” Learning Setting Inductive Transfer Learning Transductive Transfer Learning Unsupervised Transfer Learning Multi-task Learning (Source known) Self-taught Learning (Source unknown) Domain adaptation Transfer Learning YesYes Yes No No No
- 6. Impressed structure in Deep Learning • Usual Learning (Same source, Same task) 6
- 7. Impressed structure in Deep Learning • Usual Learning (Same source, Same task) - 先回憶 neural network • 就比較多條迴歸式一起算的模型 • 一個 unit = 一條迴歸式 7 線性迴歸 𝑊1 𝑋1 + 𝑊2 𝑋2 + 𝑊3 𝑋3 = 𝑓 𝑖=1 𝑛 𝑊𝑖 𝑋𝑖 = Y
- 8. Impressed structure in Deep Learning • Multi-Task Learning (Same source, Different task) - 一個模型，同時預測多個目標 8 Soft SharingHard Sharing 每個任務都有自己的模型和參數 正規化模型參數距離來保證參數相似性 降低過擬合的風險
- 9. Impressed structure in Deep Learning • Domain adoption (Different source, Same task) - 適用於初期標註資料不足、資料庫不夠完整、測試集與訓練集有差異 9 現在收集的 database 未來預測目標 Opendata (沒標註) ?
- 10. Impressed structure in Deep Learning • Domain adoption (Different source, Same task) - Train: Forward 10 features𝑓 class label 𝑦 domain label 𝑑 ⋮ feature extractor 𝐺𝑓 ; 𝜃𝑓 domain classifier 𝐺 𝑑 ; 𝜃 𝑑 label predictor 𝐺 𝑦 ; 𝜃 𝑦
- 11. Impressed structure in Deep Learning • Domain adoption (Different source, Same task) - Train: Backward 11 class label 𝑦 domain label 𝑑 ⋮ 𝜕𝐿 𝑓 𝜕𝜃 𝑓 Loss 𝐿 𝑓= 𝜕𝐿 𝑦 𝜕𝜃 𝑓 − 𝜆 𝜕𝐿 𝑑 𝜕𝜃 𝑓 𝜕𝐿 𝑦 𝜕𝜃 𝑦 Loss 𝐿 𝑦 𝜕𝐿 𝑑 𝜕𝜃 𝑑 Loss 𝐿 𝑑 features𝑓
- 12. Impressed structure in Deep Learning • Domain adoption (Different source, Same task) - Backward / Backpropagation • Adversarial - Domain classifier 跟 label predictor feature extractor 互相對抗 - Domain classifier 會幫助 label predictor 預測效能 12 Goal 2: 各別任務 loss 越小越好Goal 1: 整體 loss 越小越好 ! ! min 𝜃 𝑦 𝜕𝐿 𝑦 𝜕𝜃 𝑦 & min 𝜃 𝑑 𝜕𝐿 𝑑 𝜕𝜃 𝑑 min 𝜃 𝑓,𝜃 𝑦,𝜃 𝑑 𝜕𝐿 𝑦 𝜕𝜃𝑓 − 𝜆 𝜕𝐿 𝑑 𝜕𝜃𝑓
- 13. Impressed structure in Deep Learning • Transfer learning (Different source, Different task) - 自己運算資源、訓練模型的資料不足 - 別人已經訓練好任務更大的模型 • 例如 Google 用 ImageNet 訓練好的模型 13 我只是想分類 Dog 和 Cat
- 14. Impressed structure in Deep Learning • Transfer learning (Different source, Different task) - 把別人的 model (Pre-trained Model) 最後一層拔掉，加入新的層，然後用新資料訓練新層的參數 14
- 15. Impressed structure in Deep Learning • Transfer learning (Different source, Different task) - 把別人的 model (Pre-trained Model) 最後一層拔掉，加入新的層，然後用新資料訓練新層的參數 15
- 16. Outline • Impressed Structure In Deep Learning - Transfer learning - GAN - Reinforcement learning - Capsule, Spiking NN • Black-box • Advantage of Statistics 16
- 17. Impressed structure in Deep Learning • GAN, Generative Adversarial Network - Loss 計算相似 Domain adoption - 以 Image 為例 17 G𝒛𝑰 𝐑𝐞𝐚𝐥 𝑰’ 𝐑𝐞𝐚𝐥 G 生成器 𝒛 + 𝝈 D 分類器 𝑰 𝐅𝐚𝐜𝐤 𝑰 𝐑𝐞𝐚𝐥 Real probability Encode - Decode
- 18. Impressed structure in Deep Learning • GAN, Generative Adversarial Network 18 G 生成器 𝒛 + 𝝈 D 分類器 𝑰 𝐅𝐚𝐜𝐤 𝑰 𝐑𝐞𝐚𝐥 0.9 0.1 Real probability 0.9 0.9 Ideally In fact 0.7 0.9
- 19. Impressed structure in Deep Learning • GAN, Generative Adversarial Network - 評估生成器的能力 • 兩個(資料庫 vs 生成)分布是否相同 • Inception Score (IS) • Fréchet Inception Distance (FID) - 可以用 GAN 生成新資料嗎？ • Betwend Yes & No. 無法生成超過資料庫的特徵 • ex. 想生成頭朝左的黑馬 只有頭朝右的黑馬→ No 頭朝右的黑馬 & 頭朝左的斑馬→ Yes 19
- 20. Impressed structure in Deep Learning • AnoGAN, Anomaly GAN - 可在無異常樣本下訓練分類器區分異常、並偵測未知異常圖形 20 real difference generated Anomaly detection 異常 正常 正常時的樣貌
- 21. Impressed structure in Deep Learning • AnoGAN, Anomaly GAN - Train 的過程 • 只用正常影像即可， 𝐺 只生成正常的影像 21 𝑧 取特徵 生成影像 是否為生成 改善 𝐺 生成正常影像的能力 訓練完畢 Encode Decode 正常 正常’ Discriminate 騙成功 騙失敗
- 22. Impressed structure in Deep Learning • AnoGAN, Anomaly GAN - 用來判斷是否異常 1. 生成正常時該有的樣貌 2. 計算異常分數 22 𝑧 取特徵 生成影像 是否為生成 加噪音改變 𝑧 → 調整生成樣貌 訓練完畢 Encode Decode 異常 生成正常 Discriminate 騙成功 騙失敗
- 23. Impressed structure in Deep Learning • AnoGAN, Anomaly GAN - 用來判斷是否異常 1. 生成正常時該有的樣貌 2. 計算異常分數 23 Anomaly detection真實異常 正常時的樣貌 生成正常 difference
- 24. Outline • Impressed Structure In Deep Learning - Transfer learning - GAN - Reinforcement learning - Capsule, Spiking NN • Black-box • Advantage of Statistics 24
- 25. Impressed structure in Deep Learning • Reinforcement learning - 從現在的環境來決定行為，介於監督式和非監督式間 - 應用機器人行為：聊天、物流裝箱、競賽(ex. 打電動) 25
- 26. Impressed structure in Deep Learning • Reinforcement learning - Plays catch 26 • S, situation • A, action ■ left, stay, right • R, reward ■ 1: 接到, 0: 等待掉落, -1: 死掉 • Policy ■ Largest reward: 接到次數最多 http://edersantana.gith ub.io/articles/keras_rl/
- 27. Impressed structure in Deep Learning • Reinforcement learning - Plays catch 27 End 𝑺 𝒕 left stay right 判定 𝑅𝑡 死掉 接到 等待掉落 Existed NN 𝑺𝒕+𝟏 𝐴 𝑡
- 28. Impressed structure in Deep Learning • Reinforcement learning - Plays catch 28 𝑺 𝒕 left stay right 判定 𝑅𝑡 死掉 接到 等待掉落 Existed NN 𝑺𝒕+𝟏 𝐴 𝑡
- 29. Impressed structure in Deep Learning • Reinforcement learning - Plays catch, RL part 29 𝑺 𝒕 left stay right 判定 𝑅𝑡 死掉 接到 等待掉落 Correct pair(s) 𝑺 𝟏, 𝑨 𝟏 ′ , … , (𝑺 𝒕, 𝑨 𝒕 ′ ) Modify weight 𝑨 𝒕 ′ 𝑺𝒕+𝟏 𝐴 𝑡 Existed NN
- 30. Impressed structure in Deep Learning • Reinforcement learning - Plays catch, RL part 30 Modify 𝐴 𝑡 to 𝐴 𝑡 ′
- 31. Outline • Impressed Structure In Deep Learning - Transfer learning - GAN - Reinforcement learning - Capsule, Spiking NN • Black-box • Advantage of Statistics 31
- 32. Impressed structure in Deep Learning • Capsule and Dynamic Routing - Drawbacks of CNN • Pooling weak - Ambivalent the spatial relationships of the previous layer 32 15 9 4 2 11 5 8 6 8 7 3 17 2 3 7 3 15 8 8 17 10 5 5 8 Max pooling Average pooling Only keep partial feature Max is define by input layer, not Bi-layer 旋轉縮放和平移不變性，適應程度較小，需要大量 augmentation 協助 Weight are equal Who
- 33. Impressed structure in Deep Learning • Capsule and Dynamic Routing - 用向量代替元素輸出，可捕捉空間結構信息的膠囊概念 33 Tradition NN Capsule Net Unit: List of objects objects can be different attributes Unit: One object Ex. value, one-hot vector, pixel
- 34. Impressed structure in Deep Learning • Capsule and Dynamic Routing - 用 routing-by-agreement 取代 max-pooling 34 R G B 2 1 ⋯ ⋯ Layer I Layer JLayer A Layer B Layer C Layer ZLayer Y
- 35. Impressed structure in Deep Learning • Capsule and Dynamic Routing - 用 routing-by-agreement 取代 max-pooling 36 R G B 2 1 幾乎傳一樣 所以不能迭代太多次 R G B 2 1 1st 迭代後 R G B 2 1 2nd 迭代後 R G B 2 1 nth 迭代後 𝑊𝑖𝑗, 𝐶𝑖𝑗 𝑊𝑖𝑗: 傳統 CNN 的權重，不變 𝐶𝑖𝑗: 𝑖 對 𝑗 的影響力，迭代改變 Layer I Layer J 1 = 𝑖=𝑅,𝐺,𝐵 𝐶𝑖1 ⋅ 𝑊𝑖1 ⋅ capsule 𝑖 𝑊𝑖𝑗, 𝐶𝑖𝑗𝑊𝑖𝑗, 𝐶𝑖𝑗 𝑊𝑖𝑗, 𝐶𝑖𝑗 更新 𝐶𝑖𝑗 ⇒ 𝐶 𝑅1 ↑, 𝐶G1 ↓, 𝐶 𝐵1 ↓, 𝐶 𝑅2 ↓, 𝐶G2 ↓, 𝐶 𝐵2 ↑ 再更新 𝐶𝑖𝑗 ⇒ 𝐶 𝑅1 ↑, 𝐶G1 ↓, 𝐶 𝐵1 ↓, 𝐶 𝑅2 ↓, 𝐶G2 ↓, 𝐶 𝐵2 ↑ 新的 初始均等 定義
- 36. Impressed structure in Deep Learning • Capsule and Dynamic Routing - 用 routing-by-agreement 取代 max-pooling 37 壓縮到 [0,1) 保留向量特徵
- 37. Impressed structure in Deep Learning • Spiking NN - 從其它神經元得到的訊號，該神經元反應強度隨著輸入訊號變化 - 直到強度超過閾值後 • 才傳遞訊號給下個神經元 • 該神經元重置反應強度 38 神經元的突觸樹、軸突和細胞體
- 38. Outline • Impressed Structure In Deep Learning - Transfer learning - GAN - Reinforcement learning - Capsule, Spiking NN • Black-box • Advantage of Statistics 39
- 39. Black-box in Deep Learning 40 Predict: wolf True: wolf Predict: husky True: husky Predict: wolf True: wolf Predict: wolf True: wolf Predict: husky True: husky Predict: wolf True: husky
- 40. Black-box in Deep Learning • LIME, Local interpretable model-agnostic explanations - 透過擾動輸入樣本 (perturb the input) - 來判斷哪些特徵對辨識結果有最大的影響 41
- 41. Black-box in Deep Learning 42 Predict: wolf True: wolf Predict: husky True: husky Predict: wolf True: wolf Predict: wolf True: wolf Predict: husky True: husky Predict: wolf True: husky
- 42. Black-box in Deep Learning • LRP, Layer-wise relevance propagation - 將 loss 透過反向傳播 (backpropagation) - 來判斷每個 pixel 和 unit 與辨識結果的關聯性 43 使用已訓練好的模型權重和預測誤差 不對模型做任何改變 𝑅𝑗 = 𝑘 𝑎 𝑘 𝑤𝑗𝑘 + 𝑗 𝑎 𝑘 𝑤𝑗𝑘 + 𝑅 𝑘
- 43. Black-box in Deep Learning • LRP, Layer-wise relevance propagation - Demo website: http://heatmapping.org/mnist.html 44 已存在 model: Long ReLu 1 的判斷9 的判斷 7 的判斷
- 44. Black-box in Deep Learning • LRP, Layer-wise relevance propagation - Demo website: http://heatmapping.org/mnist.html 45
- 45. Black-box in Deep Learning • Compare • Ref: Explainable AI 是什麼？為什麼 AI 下判斷要可以解釋？ 46 改 model unit 改 image pixel 透過 loss 反傳 任意 model X V V 特定 layer V X V 特定 class X Δ V 小區域敏感度 X V X 計算速度 Δ X V Soft attention change LIME / IntGrad LRP / GradCAM
- 46. Outline • Impressed Structure In Deep Learning - Transfer learning - GAN - Reinforcement learning - Capsule, Spiking NN • Black-box • Advantage of Statistics 47
- 47. Advantage of Statistics in Deep Learning • [2019/02] 深度學習的發展遇到了 3 個瓶頸！ - Alan Yuille 電腦視覺領域奠基者表示，深度學習正面臨三大瓶頸 • 三大瓶頸：需要大量標註數據、過度擬合基準數據、對圖像變化過度敏感 • 組合爆炸：真實世界的圖像，從組合學觀點來看太大量了 任何一個數據集，不管多大，都很難表達出現實的複雜程度 - Pedro Domingos 分析 1.6 萬篇論文後表示，深度學習的發展終點將近 • [2017/12] AI 熱潮，會不會只是一場泡沫？ - 就像 100 年前的電力、20 年前的網際網路一樣，AI 也會改變每一個產業。 48
- 48. Advantage of Statistics in Deep Learning • CS vs. DS 處理問題方式 49 初階 Computer Science 1. 直接套模型 2. 參考別人調參數和改架構 3. 用更熱門的模型架構 4. 上社群、論壇求解 5. 沒轍了 初階 Data Science / Statistics 1. 分析資料型態 2. 選擇合適模型 3. 猜測可能的問題 4. 找工具 or 方法驗證猜想 5. 調整輸入資料 or 模型參數 6. 沒轍了 初階 CS 可能永久卡關 1. 數學不好 2. 不擅長資料視覺化 統計分析 初階 Statistics 較弱 • 參考 “從統計到資料科學”
- 49. Advantage of Statistics in Deep Learning • CS vs. DS 處理問題方式：用套件找邊界 (pixel 值變化) 50 找肝臟區域 改善腫瘤預測區域 同一個套件為什麼效果差這麼多？ 覺得這個套件很爛 腫瘤預測區域 改善預測區域
- 50. Advantage of Statistics in Deep Learning • CS vs. DS 處理問題方式： 51 看了幾個例子後猜測 1. FNH 在 ART vs. PV 差異較 HCC & HEM 大 2. HEM 的質地比 HCC 的不均勻 肝癌 CT 影像 你會怎麼驗證猜測？
- 51. Advantage of Statistics in Deep Learning • 我一開始做論文的處理問題方式： 52 我用折線圖，除了測站間值有高低其他都看不出來 老師：換 Box Plot 畫看看 哇!!真的有東西 為什麼同一個類型 box range 不一樣 我現在會用 group box plot 畫在一起
- 52. Outline • Impressed Structure In Deep Learning - Transfer learning - GAN - Reinforcement learning - Capsule, Spiking NN • Black-box • Advantage of Statistics 53
- 53. Q&A Thanks for your attention! 54