Pro Yearly is on sale from $80 to $50! »

Machine Learning の新機能✨ / What's New in Machine Learning

Machine Learning の新機能✨ / What's New in Machine Learning

WWDC19で発表されたMLの新機能まとめスライドです。
このスライドを見ればWWDCのML関連の話題がだいたい掴めます。

- お断り -
このスライドは一般公開されているWWDC19のスライドのみ引用しています。

iOS de KANPAI !【WWDC 2019 報告会】
15 min

F400611942a8b7a695f741f9a720fcf8?s=128

じんむ

June 14, 2019
Tweet

Transcript

  1. .BDIJOF-FBSOJOHͷ৽ػೳ✨

  2. !LPPPPPUBLF ͡ΜΉ ͓அΓ ͜ͷεϥΠυ͸Ұൠެ։͞Ε͍ͯΔ88%$ͷεϥΠυͷΈҾ༻͍ͯ͠·͢

  3. None
  4. ηογϣϯ

  5. 7JEFP63- IUUQTEFWFMPQFSBQQMFDPNWJEFPTQMBZXXED 7JEFP ৄ͘͠஌Γ͍ͨ࣌͸

  6. Summary Create ML Domain API Core ML 3 7JEFP

  7. Create ML $SFBUF.- .-ϞσϧΛ4XJGUͰ࡞Δ͜ͱ͕Ͱ͖Δ

  8. Create ML ࠓ

  9. Sound Activity Tabular Image Text Create ML ৽

  10. <9DPEF><0QFO%FWFMPQFS5PPM><$SFBUF.-> Create ML ৽

  11. Create ML ৽ υϥοάυϩοϓͰ؆୯ʹ

  12. Image Classifier Object Detector

  13. %&.0

  14. ෯޿͍.-ϞσϧΛ ؆୯ʹ࡞Δ͜ͱ͕Ͱ͖Δྫ Create ML

  15. Image Classifier Object Detector 7JEFP

  16. αΠίϩͷ໨ΛσΟςΫτ Dice Detection Model 5 5 4 4 6

  17. Sound Classifier 7JEFP

  18. ԻΛฉ͖෼͚Δ

  19. Text Classifier Word Tagger 7JEFP

  20. จষͷײ৘εύϜτϐοΫΛ෼ྨ Text Classification Topic Classification Spam/Not Spam Sentiment Analysis !

    " Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Label 1 Label 2 Label 3 Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum
  21. Tabular Classifier Tabular Regressor Recommender 7JEFP

  22. Hardtrail Mountain Aspen Valley Pleasant Cove For You: Rocky Peak

    Cactus Ridge ͜ͷࢁΛߴධՁʹͨ͠ਓʹ ࣍ʹొΔࢁΛϨίϝϯυ
  23. Activity Classifier 7JEFP

  24. "QQMF8BUDIͷηϯαΛར༻ Device Sensors g a r Accelerometer Gyroscope Magnetometer Altimeter

  25. ͲͷߦಈΛ͍ͯ͠Δ͔Λ෼ྨ Jogging Standing Gestures Gaming Golf Swimming

  26. .-Y"3 + 7JEFP

  27. "QQMF1FODJMͰखॻ͖จࣈࣝผ

  28. "3͢͝Ζ͘ͱ૊Έ߹Θͤ

  29. ✨؆୯ʹ࣮ݱͰ͖Δ✨

  30. ͡ΜΉͷਪ͠ ϑϦεϏʔͷ౤͛ํ෼ྨͷ%&.0 Frisbee Motion Classifier Hammer Bowler Chicken Wing Backhand

    Forehand 7JEFP
  31. Summary Create ML Domain API Core ML 3

  32. %PNBJO"1* ֶशࡁΈͷϞσϧѻ͑Δ

  33. Face capture quality Sound analysis Speech Saliency Speech on Mac

    Sentiment classification Animal Detection Text Recognition Object Tracking Attention Saliency Document Camera On device speech Word Tagging Image similarity Face Landmark Image Similarity Landmark Detection Image saliency Rectangle Detection Text catalog Word Embeddings Image Classification NL Transfer learning
  34. 7JTJPO'SBNFXPSL *NBHF 7JEFP

  35. ਓ͕Ͳ͜ʹண໨͢Δ͔Λֶश Attention and Objectness Based Saliency

  36. લܠͱޙܠΛֶश Attention and Objectness Based Saliency

  37. ˠը૾͔ΒॏཁͳΦϒδΣΫτΛநग़ Bounding Boxes (0,0)

  38. ը૾ͷྨࣅ౓Λܭࢉ Precision and Recall Recall Percentage of Target Class retrieved

    from entire library
  39. 'BDF-BOENBSLTͷվળ 76pt Face Landmarks 65pt Single Confidence Score Confidence Score

    per point New
  40. ਓؒݕ஌ New Detectors - Human Detector

  41.  New Detectors - Cat and Dog Detectors Cat Dog

    Dog Dog Cat
  42. τϥοΩϯάਫ਼౓ͷվળ New Object Tracker

  43. 7JTJPO'SBNFXPSL 5FYU 7JEFP

  44. จࣈ͓͜͠

  45. ଎͞WTਖ਼֬͞ Fast Versus Accurate Fast Accurate Processing time Optimized for

    real-time Asynchronous processing Memory footprint Smallest Larger Support for rotated text Limited Broad Support for variety of fonts Limited Diverse font styles Accuracy for natural language Good Best
  46. ద੾ͳύϥϝʔλઃఆΛ͢΂͠ Use case • Read codes/serial numbers just like a

    barcode reader • Constrained camera usage • Interactivity is key request = VNRecognizeTextRequest(completionHandler: recognizeTextHandler) request.recognitionLevel = .fast
  47. ӳޠͷΈ

  48. /BUVSBM-BOHVBHF 7JEFP

  49. จষͷײ৘ղੳ Natural Language Sentiment Analysis I was so excited for

    the season finale, $ Natural Language Sentiment Analysis but it was a bit disappointing. I was so excited for the season finale, $ ☹
  50. Sentiment Analysis Text Classification Sentiment Analysis -1.0 1.0 Natural Language

    0 “We had a not so fun time in Hawaii cause mom twisted her ankle.” -0.8 Natural Language Text ෛPSਖ਼ͷײ৘ͷ਺஋Խ
  51. 4QFFDIBOE4PVOE 7JEFP

  52. ΦϯσόΠεͰԻ੠ೝࣝ On-Device Device Support iPhone 6s and later iPad (5th

    generation) and later All
  53. ೔ຊޠͳ͍ On-Device Language Support English
 United States, Canada, 
 Great

    Britain, India Spanish
 United States, Mexico, 
 Spain Italian Brazilian Portuguese Russian Turkish Chinese
 Mandarin and Cantonese
  54. Ի੠ೝࣝ ˙ൃ࿩଎౓ ˙Ұ࣌ఀࢭ ˙੠ͷಛ௃ ৽

  55. ੠ͷಛ௃ ˙+JUUFS໎͍ͷ͋Δ੠ʁ ˙4IJNNFSԒͷ͋Δ੠ʁ // Printing new results when recognizing pre-recorded

    audio if result.isFinal { let formattedString = result.bestTranscription.formattedString let speakingRate = result.bestTranscription.speakingRate let averagePauseDuration = result.bestTranscription.averagePauseDuration for segment in recognitionResult.bestTranscription.segments { let jitter = segment.voiceAnalytics?.jitter.acousticFeatureValuePerFrame let shimmer = segment.voiceAnalytics?.shimmer.acousticFeatureValuePerFrame let pitch = segment.voiceAnalytics?.pitch.acousticFeatureValuePerFrame let voicing = segment.voiceAnalytics?.voicing.acousticFeatureValuePerFrame } } ৽
  56. Summary Create ML Domain API Core ML 3

  57. $PSF.- 7JEFP

  58. Core ML 3 Model Flexibility Model
 Personalization NEW

  59. Neural Network layers 100+ αϙʔτ

  60. ഑෍ϞσϧϚγϚγ

  61. ΦϯσόΠεΞοϓσʔτ Update Training examples UPDATED NEW

  62. User Differences Ñ ൚༻తͳϞσϧͰ͸ਏ͍࣌

  63. Update Task Ö ❤ á StickerClassifier Update Task CustomStickerClassifer UPDATED

    طଘͷϞσϧΛΞοϓσʔτ
  64. On-Device Privacy Available No server ΦϯσόΠεͷྑ͞

  65. 5VSJ$SFBUF 1ZUIPOͰ $PSF.-ϞσϧΛ࡞Δ͜ͱ͕Ͱ͖Δ ϥΠϒϥϦ 7JEFP

  66. ୔ࢁͷը૾͕ඞཁ Original Approach ࠓ

  67. Ұຕͷը૾Ͱ෺ମݕ஌ Synthetic Data Augmentation ৽

  68. Synthetic Data Augmentation ৽ Ұຕͷը૾Ͱ෺ମݕ஌

  69. None
  70. 88%$ʹࢀՃ͢Δ͔೰ΜͰ͍Δਓ޲͚ IUUQTOPUFNVLPPPPPUBLFOOFBF

  71. !LPPPPPUBLF ͡ΜΉ

  72. %F/"͸ɺຊΠϕϯτͷ಺༰ɺฒͼʹ͓٬༷͕ຊΠϕϯτΛ௨ͯ͡ೖखͨ͠৘ใ౳ʹ͍ͭͯɺ ͦͷ׬શੑɺਖ਼֬ੑɺ࣮֬ੑɺ༗༻ੑ౳ʹ͖ͭɺ͍͔ͳΔ੹೚΋ෛΘͳ͍΋ͷͱ͠·͢ɻ