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Niloy Mitra Iasonas Kokkinos Paul Guerrero Vladimir Kim Kostas Rematas Tobias Ritschel UCL UCL/Facebook UCL Adobe Research U Washington UCL Deep Learning for Graphics

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Page 1: Deep Learning for Graphics - UCL · Course: “Deep Learning for Graphics” End-to-end: Loss • Old days • Evaluation came after • It was a bit optional: • You might still

NiloyMitra IasonasKokkinos PaulGuerrero VladimirKim KostasRematas TobiasRitschel

UCL UCL/Facebook UCL AdobeResearch UWashington UCL

DeepLearningforGraphics

DeepLearningforGraphics

Page 2: Deep Learning for Graphics - UCL · Course: “Deep Learning for Graphics” End-to-end: Loss • Old days • Evaluation came after • It was a bit optional: • You might still

Course:“DeepLearningforGraphics”

People

�2

NiloyMitra

Page 3: Deep Learning for Graphics - UCL · Course: “Deep Learning for Graphics” End-to-end: Loss • Old days • Evaluation came after • It was a bit optional: • You might still

Course:“DeepLearningforGraphics”

People

�2

NiloyMitra IasonasKokkinos

Page 4: Deep Learning for Graphics - UCL · Course: “Deep Learning for Graphics” End-to-end: Loss • Old days • Evaluation came after • It was a bit optional: • You might still

Course:“DeepLearningforGraphics”

People

�2

NiloyMitra IasonasKokkinos PaulGuerrero

Page 5: Deep Learning for Graphics - UCL · Course: “Deep Learning for Graphics” End-to-end: Loss • Old days • Evaluation came after • It was a bit optional: • You might still

Course:“DeepLearningforGraphics”

People

�2

NiloyMitra IasonasKokkinos PaulGuerrero VladimirKim

Page 6: Deep Learning for Graphics - UCL · Course: “Deep Learning for Graphics” End-to-end: Loss • Old days • Evaluation came after • It was a bit optional: • You might still

Course:“DeepLearningforGraphics”

People

�2

NiloyMitra IasonasKokkinos PaulGuerrero VladimirKim KostasRematas

Page 7: Deep Learning for Graphics - UCL · Course: “Deep Learning for Graphics” End-to-end: Loss • Old days • Evaluation came after • It was a bit optional: • You might still

Course:“DeepLearningforGraphics”

People

�2

NiloyMitra IasonasKokkinos PaulGuerrero VladimirKim KostasRematas TobiasRitschel

Page 8: Deep Learning for Graphics - UCL · Course: “Deep Learning for Graphics” End-to-end: Loss • Old days • Evaluation came after • It was a bit optional: • You might still

Course:“DeepLearningforGraphics”

CourseOverview

�3

Page 9: Deep Learning for Graphics - UCL · Course: “Deep Learning for Graphics” End-to-end: Loss • Old days • Evaluation came after • It was a bit optional: • You might still

Course:“DeepLearningforGraphics”

CourseOverview• PartI:IntroductionandMLBasics

�3

Page 10: Deep Learning for Graphics - UCL · Course: “Deep Learning for Graphics” End-to-end: Loss • Old days • Evaluation came after • It was a bit optional: • You might still

Course:“DeepLearningforGraphics”

CourseOverview• PartI:IntroductionandMLBasics

• PartII:SupervisedNeuralNetworks:TheoryandApplications

�3

Page 11: Deep Learning for Graphics - UCL · Course: “Deep Learning for Graphics” End-to-end: Loss • Old days • Evaluation came after • It was a bit optional: • You might still

Course:“DeepLearningforGraphics”

CourseOverview• PartI:IntroductionandMLBasics

• PartII:SupervisedNeuralNetworks:TheoryandApplications

• PartIII:UnsupervisedNeuralNetworks:TheoryandApplications

�3

Page 12: Deep Learning for Graphics - UCL · Course: “Deep Learning for Graphics” End-to-end: Loss • Old days • Evaluation came after • It was a bit optional: • You might still

Course:“DeepLearningforGraphics”

CourseOverview• PartI:IntroductionandMLBasics

• PartII:SupervisedNeuralNetworks:TheoryandApplications

• PartIII:UnsupervisedNeuralNetworks:TheoryandApplications

• PartIV:BeyondImageData

�3

Page 13: Deep Learning for Graphics - UCL · Course: “Deep Learning for Graphics” End-to-end: Loss • Old days • Evaluation came after • It was a bit optional: • You might still

Course:“DeepLearningforGraphics”

CourseOverview• PartI:IntroductionandMLBasics

• PartII:SupervisedNeuralNetworks:TheoryandApplications

• PartIII:UnsupervisedNeuralNetworks:TheoryandApplications

• PartIV:BeyondImageData

�3

Four1.5hrsessionswithbreaksasperEGtimetable.

Page 14: Deep Learning for Graphics - UCL · Course: “Deep Learning for Graphics” End-to-end: Loss • Old days • Evaluation came after • It was a bit optional: • You might still

Code ExamplesPCA/SVD basis Linear Regression Polynomial Regression Stochastic Gradient Descent vs. Gradient DescentMulti-layer Perceptron Edge Filter ‘Network’ Convolutional Network Filter Visualization Weight Initialization Strategies Colorization Network Autoencoder Variational Autoencoder Generative Adversarial Network

�4http://geometry.cs.ucl.ac.uk/dl4g/

Page 15: Deep Learning for Graphics - UCL · Course: “Deep Learning for Graphics” End-to-end: Loss • Old days • Evaluation came after • It was a bit optional: • You might still

EGCourseDeepLearningforGraphics

Two-wayCommunication

�5

Page 16: Deep Learning for Graphics - UCL · Course: “Deep Learning for Graphics” End-to-end: Loss • Old days • Evaluation came after • It was a bit optional: • You might still

EGCourseDeepLearningforGraphics

Two-wayCommunication• Thistutorialisgivenforthefirsttime!

�5

Page 17: Deep Learning for Graphics - UCL · Course: “Deep Learning for Graphics” End-to-end: Loss • Old days • Evaluation came after • It was a bit optional: • You might still

EGCourseDeepLearningforGraphics

Two-wayCommunication• Thistutorialisgivenforthefirsttime!

• Ouraimistoconveywhatwefoundtoberelevantsofar.

�5

Page 18: Deep Learning for Graphics - UCL · Course: “Deep Learning for Graphics” End-to-end: Loss • Old days • Evaluation came after • It was a bit optional: • You might still

EGCourseDeepLearningforGraphics

Two-wayCommunication• Thistutorialisgivenforthefirsttime!

• Ouraimistoconveywhatwefoundtoberelevantsofar.

• Youareinvited/encouragedtogivefeedback• On-lineform• Speakup.Pleasesendusyourcriticism/comments/suggestions• Askquestions,please!

�5

Page 19: Deep Learning for Graphics - UCL · Course: “Deep Learning for Graphics” End-to-end: Loss • Old days • Evaluation came after • It was a bit optional: • You might still

EGCourseDeepLearningforGraphics

Two-wayCommunication• Thistutorialisgivenforthefirsttime!

• Ouraimistoconveywhatwefoundtoberelevantsofar.

• Youareinvited/encouragedtogivefeedback• On-lineform• Speakup.Pleasesendusyourcriticism/comments/suggestions• Askquestions,please!

• Thankstomanypeoplewhohelpedsofarwithslides/comments.

�5

Page 20: Deep Learning for Graphics - UCL · Course: “Deep Learning for Graphics” End-to-end: Loss • Old days • Evaluation came after • It was a bit optional: • You might still

Course:“DeepLearningforGraphics”

CourseOverview• PartI:IntroductionandMLBasics

• PartII:SupervisedNeuralNetworks:TheoryandApplications

• PartIII:UnsupervisedNeuralNetworks:TheoryandApplications

• PartIV:BeyondImageData

�6

Page 21: Deep Learning for Graphics - UCL · Course: “Deep Learning for Graphics” End-to-end: Loss • Old days • Evaluation came after • It was a bit optional: • You might still

Course:“DeepLearningforGraphics”

RepresentationsinCG

�7

Page 22: Deep Learning for Graphics - UCL · Course: “Deep Learning for Graphics” End-to-end: Loss • Old days • Evaluation came after • It was a bit optional: • You might still

Course:“DeepLearningforGraphics”

RepresentationsinCG• Images(e.g.,pixelgrid)

�7

Page 23: Deep Learning for Graphics - UCL · Course: “Deep Learning for Graphics” End-to-end: Loss • Old days • Evaluation came after • It was a bit optional: • You might still

Course:“DeepLearningforGraphics”

RepresentationsinCG• Images(e.g.,pixelgrid)

• Volume(e.g.,voxelgrid)

�7

Page 24: Deep Learning for Graphics - UCL · Course: “Deep Learning for Graphics” End-to-end: Loss • Old days • Evaluation came after • It was a bit optional: • You might still

Course:“DeepLearningforGraphics”

RepresentationsinCG• Images(e.g.,pixelgrid)

• Volume(e.g.,voxelgrid)

• Meshes(e.g.,vertices/edges/faces)

�7

Page 25: Deep Learning for Graphics - UCL · Course: “Deep Learning for Graphics” End-to-end: Loss • Old days • Evaluation came after • It was a bit optional: • You might still

Course:“DeepLearningforGraphics”

RepresentationsinCG• Images(e.g.,pixelgrid)

• Volume(e.g.,voxelgrid)

• Meshes(e.g.,vertices/edges/faces)

• Animation(e.g.,skeletalpositionsovertime;clothdynamicsovertime)

�7

Page 26: Deep Learning for Graphics - UCL · Course: “Deep Learning for Graphics” End-to-end: Loss • Old days • Evaluation came after • It was a bit optional: • You might still

Course:“DeepLearningforGraphics”

RepresentationsinCG• Images(e.g.,pixelgrid)

• Volume(e.g.,voxelgrid)

• Meshes(e.g.,vertices/edges/faces)

• Animation(e.g.,skeletalpositionsovertime;clothdynamicsovertime)

• Pointclouds(e.g.,pointarrays)

�7

Page 27: Deep Learning for Graphics - UCL · Course: “Deep Learning for Graphics” End-to-end: Loss • Old days • Evaluation came after • It was a bit optional: • You might still

Course:“DeepLearningforGraphics”

RepresentationsinCG• Images(e.g.,pixelgrid)

• Volume(e.g.,voxelgrid)

• Meshes(e.g.,vertices/edges/faces)

• Animation(e.g.,skeletalpositionsovertime;clothdynamicsovertime)

• Pointclouds(e.g.,pointarrays)

• Physicssimulations(e.g.,fluidflowoverspace/time)

�7

Page 28: Deep Learning for Graphics - UCL · Course: “Deep Learning for Graphics” End-to-end: Loss • Old days • Evaluation came after • It was a bit optional: • You might still

Course:“DeepLearningforGraphics”

ProblemsinComputerGraphics• Featuredetection(imagefeatures,pointfeatures)

• Denoising,Smoothing,etc.

• Embedding,Distancecomputation

• Rendering

• Animation

• Physicalsimulation

• Generativemodels

�8

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Rm⇥m ! Rm⇥m<latexit sha1_base64="ZtRsz5108GrjlMV7h9Woj+OVox0=">AAACJnicbVDLSgNBEJz1GeMr6tHLYBA8hV0R9CIEvXiMYh6QjWF2MpsMmZldZnqVsOzXePFXvHiIiHjzU5w8QJPY0FBUVdPdFcSCG3DdL2dpeWV1bT23kd/c2t7ZLezt10yUaMqqNBKRbgTEMMEVqwIHwRqxZkQGgtWD/vVIrz8ybXik7mEQs5YkXcVDTglYql249CWBXhCkd9lDKrEPXDKDZYZ9zbs9IFpHTzj/a8Izrnah6JbcceFF4E1BEU2r0i4M/U5EE8kUUEGMaXpuDK2UaOBUsCzvJ4bFhPZJlzUtVMTuaaXjNzN8bJkODiNtWwEes38nUiKNGcjAOkf3mnltRP6nNRMIL1opV3ECTNHJojARGCI8ygx3uGYUxMACQjW3t2LaI5pQsMnmbQje/MuLoHZa8tySd3tWLF9N48ihQ3SETpCHzlEZ3aAKqiKKntErGqJ358V5cz6cz4l1yZnOHKCZcr5/ABkhpiA=</latexit><latexit sha1_base64="ZtRsz5108GrjlMV7h9Woj+OVox0=">AAACJnicbVDLSgNBEJz1GeMr6tHLYBA8hV0R9CIEvXiMYh6QjWF2MpsMmZldZnqVsOzXePFXvHiIiHjzU5w8QJPY0FBUVdPdFcSCG3DdL2dpeWV1bT23kd/c2t7ZLezt10yUaMqqNBKRbgTEMMEVqwIHwRqxZkQGgtWD/vVIrz8ybXik7mEQs5YkXcVDTglYql249CWBXhCkd9lDKrEPXDKDZYZ9zbs9IFpHTzj/a8Izrnah6JbcceFF4E1BEU2r0i4M/U5EE8kUUEGMaXpuDK2UaOBUsCzvJ4bFhPZJlzUtVMTuaaXjNzN8bJkODiNtWwEes38nUiKNGcjAOkf3mnltRP6nNRMIL1opV3ECTNHJojARGCI8ygx3uGYUxMACQjW3t2LaI5pQsMnmbQje/MuLoHZa8tySd3tWLF9N48ihQ3SETpCHzlEZ3aAKqiKKntErGqJ358V5cz6cz4l1yZnOHKCZcr5/ABkhpiA=</latexit><latexit sha1_base64="ZtRsz5108GrjlMV7h9Woj+OVox0=">AAACJnicbVDLSgNBEJz1GeMr6tHLYBA8hV0R9CIEvXiMYh6QjWF2MpsMmZldZnqVsOzXePFXvHiIiHjzU5w8QJPY0FBUVdPdFcSCG3DdL2dpeWV1bT23kd/c2t7ZLezt10yUaMqqNBKRbgTEMMEVqwIHwRqxZkQGgtWD/vVIrz8ybXik7mEQs5YkXcVDTglYql249CWBXhCkd9lDKrEPXDKDZYZ9zbs9IFpHTzj/a8Izrnah6JbcceFF4E1BEU2r0i4M/U5EE8kUUEGMaXpuDK2UaOBUsCzvJ4bFhPZJlzUtVMTuaaXjNzN8bJkODiNtWwEes38nUiKNGcjAOkf3mnltRP6nNRMIL1opV3ECTNHJojARGCI8ygx3uGYUxMACQjW3t2LaI5pQsMnmbQje/MuLoHZa8tySd3tWLF9N48ihQ3SETpCHzlEZ3aAKqiKKntErGqJ358V5cz6cz4l1yZnOHKCZcr5/ABkhpiA=</latexit><latexit sha1_base64="ZtRsz5108GrjlMV7h9Woj+OVox0=">AAACJnicbVDLSgNBEJz1GeMr6tHLYBA8hV0R9CIEvXiMYh6QjWF2MpsMmZldZnqVsOzXePFXvHiIiHjzU5w8QJPY0FBUVdPdFcSCG3DdL2dpeWV1bT23kd/c2t7ZLezt10yUaMqqNBKRbgTEMMEVqwIHwRqxZkQGgtWD/vVIrz8ybXik7mEQs5YkXcVDTglYql249CWBXhCkd9lDKrEPXDKDZYZ9zbs9IFpHTzj/a8Izrnah6JbcceFF4E1BEU2r0i4M/U5EE8kUUEGMaXpuDK2UaOBUsCzvJ4bFhPZJlzUtVMTuaaXjNzN8bJkODiNtWwEes38nUiKNGcjAOkf3mnltRP6nNRMIL1opV3ECTNHJojARGCI8ygx3uGYUxMACQjW3t2LaI5pQsMnmbQje/MuLoHZa8tySd3tWLF9N48ihQ3SETpCHzlEZ3aAKqiKKntErGqJ358V5cz6cz4l1yZnOHKCZcr5/ABkhpiA=</latexit>

R3m⇥t ! R3m<latexit sha1_base64="nQCbUsp0ev/R3gsOW4YDsknbwD4=">AAACH3icbVDLSsNAFJ3UV42vqEs3g0VwVRIVdVl047KKrYWmlsl00g6dPJi5UUrIn7jxV9y4UETc9W+ctAG19cDA4ZxzmXuPFwuuwLbHRmlhcWl5pbxqrq1vbG5Z2ztNFSWSsgaNRCRbHlFM8JA1gINgrVgyEniC3XnDy9y/e2BS8Si8hVHMOgHph9znlICWutapGxAYeF56k92nxwF2gQdMYciwK3l/AETK6BGbPymcx7KuVbGr9gR4njgFqaAC9a715fYimgQsBCqIUm3HjqGTEgmcCpaZbqJYTOiQ9Flb05DoLTrp5L4MH2ilh/1I6hcCnqi/J1ISKDUKPJ3M91SzXi7+57UT8M87KQ/jBFhIpx/5icAQ4bws3OOSURAjTQiVXO+K6YBIQkFXauoSnNmT50nzqOrYVef6pFK7KOoooz20jw6Rg85QDV2hOmogip7QC3pD78az8Wp8GJ/TaMkoZnbRHxjjb03foxw=</latexit><latexit sha1_base64="nQCbUsp0ev/R3gsOW4YDsknbwD4=">AAACH3icbVDLSsNAFJ3UV42vqEs3g0VwVRIVdVl047KKrYWmlsl00g6dPJi5UUrIn7jxV9y4UETc9W+ctAG19cDA4ZxzmXuPFwuuwLbHRmlhcWl5pbxqrq1vbG5Z2ztNFSWSsgaNRCRbHlFM8JA1gINgrVgyEniC3XnDy9y/e2BS8Si8hVHMOgHph9znlICWutapGxAYeF56k92nxwF2gQdMYciwK3l/AETK6BGbPymcx7KuVbGr9gR4njgFqaAC9a715fYimgQsBCqIUm3HjqGTEgmcCpaZbqJYTOiQ9Flb05DoLTrp5L4MH2ilh/1I6hcCnqi/J1ISKDUKPJ3M91SzXi7+57UT8M87KQ/jBFhIpx/5icAQ4bws3OOSURAjTQiVXO+K6YBIQkFXauoSnNmT50nzqOrYVef6pFK7KOoooz20jw6Rg85QDV2hOmogip7QC3pD78az8Wp8GJ/TaMkoZnbRHxjjb03foxw=</latexit><latexit sha1_base64="nQCbUsp0ev/R3gsOW4YDsknbwD4=">AAACH3icbVDLSsNAFJ3UV42vqEs3g0VwVRIVdVl047KKrYWmlsl00g6dPJi5UUrIn7jxV9y4UETc9W+ctAG19cDA4ZxzmXuPFwuuwLbHRmlhcWl5pbxqrq1vbG5Z2ztNFSWSsgaNRCRbHlFM8JA1gINgrVgyEniC3XnDy9y/e2BS8Si8hVHMOgHph9znlICWutapGxAYeF56k92nxwF2gQdMYciwK3l/AETK6BGbPymcx7KuVbGr9gR4njgFqaAC9a715fYimgQsBCqIUm3HjqGTEgmcCpaZbqJYTOiQ9Flb05DoLTrp5L4MH2ilh/1I6hcCnqi/J1ISKDUKPJ3M91SzXi7+57UT8M87KQ/jBFhIpx/5icAQ4bws3OOSURAjTQiVXO+K6YBIQkFXauoSnNmT50nzqOrYVef6pFK7KOoooz20jw6Rg85QDV2hOmogip7QC3pD78az8Wp8GJ/TaMkoZnbRHxjjb03foxw=</latexit><latexit sha1_base64="nQCbUsp0ev/R3gsOW4YDsknbwD4=">AAACH3icbVDLSsNAFJ3UV42vqEs3g0VwVRIVdVl047KKrYWmlsl00g6dPJi5UUrIn7jxV9y4UETc9W+ctAG19cDA4ZxzmXuPFwuuwLbHRmlhcWl5pbxqrq1vbG5Z2ztNFSWSsgaNRCRbHlFM8JA1gINgrVgyEniC3XnDy9y/e2BS8Si8hVHMOgHph9znlICWutapGxAYeF56k92nxwF2gQdMYciwK3l/AETK6BGbPymcx7KuVbGr9gR4njgFqaAC9a715fYimgQsBCqIUm3HjqGTEgmcCpaZbqJYTOiQ9Flb05DoLTrp5L4MH2ilh/1I6hcCnqi/J1ISKDUKPJ3M91SzXi7+57UT8M87KQ/jBFhIpx/5icAQ4bws3OOSURAjTQiVXO+K6YBIQkFXauoSnNmT50nzqOrYVef6pFK7KOoooz20jw6Rg85QDV2hOmogip7QC3pD78az8Wp8GJ/TaMkoZnbRHxjjb03foxw=</latexit>

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Data-drivenAlgorithms(Supervised)

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Data-drivenAlgorithms(Supervised)

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Data-drivenAlgorithms(Supervised)

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Data-drivenAlgorithms(Supervised)

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Data-drivenAlgorithms(Unsupervised)

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VariousMLApproaches(Supervisedapproaches)

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VariousMLApproaches(Supervisedapproaches)

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VariousMLApproaches(Supervisedapproaches)

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VariousMLApproaches(Supervisedapproaches)

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VariousMLApproaches(Supervisedapproaches)

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RiseofLearning

• 1958: Perceptron• 1974: Backpropagation• 1981: Hubel&WieselwinsNobelprizefor‘visualsystem’• 1990s: SVMera• 1998: CNNusedforhandwritinganalysis• 2012: AlexNetwinsImageNet

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WhatisSpecialaboutGraphics?

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WhatisSpecialaboutGraphics?• ImageProcessing(imagetranslationtasks)

• Manysourcesofinputdata—modelbuilding (e.g.,images,scanners,motioncapture)

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Course:“DeepLearningforGraphics”

WhatisSpecialaboutGraphics?• ImageProcessing(imagetranslationtasks)

• Manysourcesofinputdata—modelbuilding (e.g.,images,scanners,motioncapture)

• Manysourcesofsyntheticdata—canserveassupervisiondata(e.g.,rendering,animation)

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Course:“DeepLearningforGraphics”

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• Manysourcesofinputdata—modelbuilding (e.g.,images,scanners,motioncapture)

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• Manyproblemsingenerativemodels

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Course:“DeepLearningforGraphics”

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• Now• End-to-end• Moveawayfromhand-craftedrepresentations

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Course:“DeepLearningforGraphics”

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Course:“DeepLearningforGraphics”

End-to-end:Loss• Olddays• Evaluationcameafter• Itwasabitoptional:

• Youmightstillhaveagoodalgorithmwithoutagoodwayofquantifyingit• Evaluationhelpedpublishing

• Now• Itisessentialandbuild-in• Ifthelossisnotgood,theresultisnotgood• Evaluationhappensautomatically

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Course:“DeepLearningforGraphics”

End-to-end:Loss• Olddays• Evaluationcameafter• Itwasabitoptional:

• Youmightstillhaveagoodalgorithmwithoutagoodwayofquantifyingit• Evaluationhelpedpublishing

• Now• Itisessentialandbuild-in• Ifthelossisnotgood,theresultisnotgood• Evaluationhappensautomatically

• Whilestillmuchislefttodo,thismakesgraphicsmuchmorereproducable

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Course:“DeepLearningforGraphics”

End-to-end:Data• Olddays• Testwithsometoyexamples• Deployonrealstuff• Maybecollectsomedatalater

• Now• Testanddeployneedtobeasidenticalasyoucan• Needtocollectdatafirst• Notwosteps

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Page 63: Deep Learning for Graphics - UCL · Course: “Deep Learning for Graphics” End-to-end: Loss • Old days • Evaluation came after • It was a bit optional: • You might still

Course:“DeepLearningforGraphics”

ExamplesinGraphics

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Rendering

Imagemanipulation

Geometry

Animation

Page 64: Deep Learning for Graphics - UCL · Course: “Deep Learning for Graphics” End-to-end: Loss • Old days • Evaluation came after • It was a bit optional: • You might still

Course:“DeepLearningforGraphics”

ExamplesinGraphics

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Sketchsimplification

Colorization

Meshsegmentation

Real-timerendering

Denoising

Boxification

Proceduralmodelling

BRDFestimation

Facialanimation

Animation

Fluid

Rendering

Imagemanipulation

Geometry

Animation

PCDprocessing

Learningdeformations

Page 65: Deep Learning for Graphics - UCL · Course: “Deep Learning for Graphics” End-to-end: Loss • Old days • Evaluation came after • It was a bit optional: • You might still

Course:“DeepLearningforGraphics”

ExamplesinGraphics

�22

Sketchsimplification

Colorization

Meshsegmentation

Real-timerendering

Denoising

Boxification

Proceduralmodelling

BRDFestimation

Facialanimation

Animation

Fluid

PCDprocessing

Learningdeformations

Page 66: Deep Learning for Graphics - UCL · Course: “Deep Learning for Graphics” End-to-end: Loss • Old days • Evaluation came after • It was a bit optional: • You might still

EGCourseDeepLearningforGraphics

CourseInformation(slides/code/comments)

http://geometry.cs.ucl.ac.uk/dl4g/

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Deep Learning for Graphics Supervised Applicationsgeometry.cs.ucl.ac.uk/dl4g/slides/part4_Supervised... · 2018. 4. 18. · EG Course Deep Learning for Graphics Object Detection:

End-To-End Learning of Geometry and Context for Deep ...openaccess.thecvf.com/content_ICCV_2017/papers/... · End-to-End Learning of Geometry and Context for Deep Stereo Regression

Surface and deep structures in graphics … and deep structures in graphics comprehension Wolfgang Schnotz & Christiane Baadte Published online: 3 December 2014 # Psychonomic Society,

CreativeAI: Deep Learning for Graphics Motion & Physics · CreativeAI: Deep Learning for Graphics Motion & Physics. ... SIGGRAPH Asia Course CreativeAI: Deep Learning for Graphics

Agile Autonomous Driving using End-to-End Deep …bboots/files/Deep...Agile Autonomous Driving using End-to-End Deep Imitation Learning Yunpeng Panz, Ching-An Cheng , Kamil Saigol

Learning Deep Structured Active Contours End-to-End...Learning deep structured active contours end-to-end Diego Marcos, Devis Tuia, Benjamin Kellenberger University of Wageningen,

End-to-End Signal Processing and Deep Learning

Large-scale Deep Unsupervised Learning using Graphics Processors

Web Offset Printing Machines & Parts by Deep Graphics Faridabad

Deep Learning for Graphics Unsupervised Learning · EG Course “Deep Learning for Graphics” InfoGAN mple:generator:discriminator maximize mutual information varying Image Credit:

End of Poverty Deep Ecology

Deep Convolutional Inverse Graphics Networkpapers.nips.cc/paper/5851-deep-convolutional-inverse-graphics-network.pdfDeep Convolutional Inverse Graphics Network Tejas D. Kulkarni*1,

Deep Learning for Natural Language processingufal.mff.cuni.cz/~zabokrtsky/fel/slides/lect13-deep-learning-nlp.pdf · Deep Learning in NLP • NLP tasks learn end-to-end using deep

End to end training with deep visiomotor

Deep End Report 12

CONFIDENTIAL Deep Dive: A SOA Application, End-to-End Deep Dive: A SOA Application, End-to-End

CS 6501: Deep Learning for Computer Graphics Basics of ...connelly/class/2016/deep_learning_graphics/03... · CS 6501: Deep Learning for Computer Graphics Basics of Neural Networks

Off The Deep End

End-to-End Deep Structured Models for Drawing Crosswalks

Deep Neural Network Algorithms on Graphics Processors for ... · SchoolofComputerScienceandStatistics Deep Neural Network Algorithms on Graphics Processors for Embedded Systems DavidCronin

Deep Learning for End-to-End Automatic Target Recognition ... · Deep Learning for End-to-End Automatic Target Recognition from Synthetic Aperture Radar Imagery Hidetoshi FURUKAWA

Bringing High-End Graphics to Handheld Devices

Deep Convolutional Inverse Graphics Network

Agile Autonomous Driving using End-to-End Deep Imitation ...roboticsproceedings.org/rss14/p56.pdf · Agile Autonomous Driving using End-to-End Deep Imitation Learning Yunpeng Panz,

Deep End Magazine

Effective End-to-End Deep Learning Process in Medical

Deep End GP Pioneer scheme - University of Glasgow · Deep End Pioneer Scheme • 5 Early career GP Fellows • 6 Deep End practices • 2 years ‘Key ingredients’: –Additional

WarpDrive: Extremely Fast End-to-End Deep Multi-Agent

GPs at the Deep End

3D Graphics Hardware - cs.cmu.edu · 3D Graphics Hardware 15-463 Graphics II Spring 1999. Topics Graphics Architecture Uniprocessor Acceleration Front-End Multiprocessing – Pipelined

End-To-End 3D Face Reconstruction With Deep Neural Networksopenaccess.thecvf.com/content_cvpr_2017/papers/Dou... · End-to-end 3D face reconstruction with deep neural networks

Deep Dive into Microsoft Silverlight Graphics

DLBooster: Boosting End-to-End Deep Learning Workflows

CS 6501: Deep Learning for Computer Graphics Introduction ...connelly/class/2016/deep_learning_graphics/01... · ... Deep Learning for Computer Graphics Introduction / Overview

Deep Convolutional Inverse Graphics Network - arXiv · Deep Convolutional Inverse Graphics Network Tejas D ... decoder networks to enable ... the basic structure of the Deep Convolutional