Article presented at the RecSysKTL workshop, co-located at ACM RecSys 2018

1. Do Better ImageNet Models Transfer
Better … for Image Recommendation?
FelipedelRío,PabloMessina,VicenteDominguez,DenisParra
CS Department
Schoolof Engineering
PontificiaUniversidadCatólicadeChile
KTLRecSysWorkshop,6de Octubrede 2018

2. Artwork Recommendation
• Online artwork market: Growing since 2008, despite
global crises!
– In 2011, art received $11.57 billion in totalglobal annual
revenue, over $2 billion versus 2010 (*forbes)
• Previous recommendation projects date for as long as
2007, such as the CHIP project to recommend paintings
from Rijksmuseum.
• Little use of recent advances in Deep Neural Networks
for Computer Vision.
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[forbes] The World’s Strongest Economy? the Global Art Market. https://www.forbes.com/sites/abigailesman/2012/02/29/the- worlds- strongest-
economy- the- global- art- market/ (2012)

3. Image Recommendation
• Since 2017 we have been working on
recommending art images, using data from the
online store UGallery.
• Two papers published:
– DLRS 2017: Dominguez,V., Messina, P., Parra,D., Mery,D., Trattner,
C., & Soto,A. (2017, August). ComparingNeural and Attractiveness-
based Visual Features for ArtworkRecommendation.In Proceedingsof
the 2nd WorkshoponDeep Learning for RecommenderSystems(pp. 55-59).
ACM.
– UMUAI2018: Messina, P., Dominguez,V., Parra,D., Trattner,C., &
Soto, A. (2018). Content-basedartworkrecommendation:integrating
painting metadata with neural and manually-engineeredvisualfeatures.
User Modelingand User-AdaptedInteraction,1-40.
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4. Data: UGallery
• Online Artwork Store, based on CA, USA.
• Mostly sales one-of-a-kind physical artwork.
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5. Image Recommendation
• Our top approach is a hybrid recommender, based
on metadata and visual features from Deep
Convolutional Neural Networks.
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6. Motivation
• When submitting our work we usually received
criticism for not using the latest DNN model.
• An actual review from a previous article submission
(2017):
<< Overall an interesting paper although … the
choice of AlexNet is rather odd as there are better
pre-trained networks available e.g. VGG16 >>
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7. Motivation
• Is it always the case that better pre-trained deep
convolutional models (on the Imagenet Challenge)
produce better results in a transfer learning setting?
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8. ImageNet:
Crowdsourcing a Large Dataset
of Image Labels

9. Datasets in Computer Vision
• 1996: faces and cars 14,000 images of 10,000 people
• 1998: MNIST 70,000 images of handwritten digits
• 2004: Caltech 101, 9,146 images of 101 categories
• 2005: PASCAL VOC 20,000 images with 20 classes
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10. Datasets in Computer Vision
• Imagenet: Presented in 2009 at CVPR
• Crowdsourced
• 14,197,122 images
• 21,841 categories (non-empty synsets)
• Categories based on WordNet taxonomy
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11. WordNet
• Wordnet: Miller’s project started in 1980 at
Princeton, a hierarchy for the English language
• Prof. Fei-Fei Li (UIUC, Princeton, Stanford),
worked on filling WordNet with many images.
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12. Crowdsourced
• Amazon Mechanical Turk
• It took 2.5 years to complete. Originally 3.2 million
images in 5,247 categories (mammal, vehicle, etc.)
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13. ImageNet Challenge
• The dataset was used to
set a competition for
image classification:
from 2010 on.
• In 2012 a team used
deep learning, got error
rate below 25% (Hinton
et al.), 10.8 point
margin, 41% better than
next best.
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14. Transfer Learning
• 2012 model was called AlexNet: a Convolutional
Neural Network
• The features learned (fc6, fc7) have been used in
succesfully, allowing to transfer the learning to
other tasks.
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15. Recent Imagenet results
https://github.com/tensorflow/models/tree/master/research/slim#pre-trained-models
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Method Top-1 Accuracy Top-5 Accuracy
NASNet Large 82.7 96.2
InceptionResNetV2 80.4 95.3
InceptionV3 78.0 93.9
ResNet50 75.6 92.8
VGG19 71.1 89.8

16. Inspiration
• Simon Kornblith, Jonathon Shlens, and Quoc V. Le.
2018. Do Better ImageNetModels Transfer Better?
(2018). https://arxiv.org/abs/1805.08974
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Without tuning
ResNet outperforms NASNet (SOTA)

17. Evaluation 1
• Do pre-trained ImageNet model performance
correlate with Image recommendation
performance?
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18. Ugallery Data and Evaluation
• 1,371 users / 3,940 items / 2,846 transactions
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19. Recommendation
• Scoring items based on cosine similarity between
user model and item model:
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20. Experiment 1: Results
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21. Experiment 1: Results
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• No correlation between ImageNet performance and
image recommendation performance.

22. Experiment 2
• What is the effect of fine-tuning?
• How should fine-tuning be performed?
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23. Tuning I: Shallow vs. Deep
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Shallow fine-tuning

24. Tuning I: Shallow vs. Deep
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Deep fine tuning

25. Learning: Multitask vs. Single Task
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• Dataset 1: Omniart
– 432,217 images
– Target classes: artist, artwork type, year
• Dataset 2: Ugallery
– 3,940 images
– Target classes: artist, medium (oil, acrylic, etc.)

26. Omniart Dataset
• http://isis-data.science.uva.nl/strezoski/#3
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27. Results 1
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Deep fine-tuning worked better than shallow fine tuning

28. Results 2
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• ResNet was better than shallow fine-tuning
• Consistent with Kornblith et al., ResNet is the best generic
visual feature extractor

29. Results 3
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• Training with a smaller but focused target dataset results in better
transfer learning performance

30. Results 4
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• There was not a clear winner between multitask and single task,
probably because the artist category is really descriptive

31. Conclusion
• Pre-trained neural image embeddings are great, but
do not assume that performance in the original task
is correlated with a current recommendation task.
• If you are still going to used a pre-trained Imagenet
visual embedding, ResNet is a good option,
although is not the current SOTA in ILSVRC.
• Fine-tuning is strongly suggested, even if your
dataset is small,
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32. THANKS!
dparra@ing.puc.cl