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Learning visual representations for unfamiliar environments Kate Saenko, Brian Kulis, Trevor Darrell UC Berkeley EECS & ICSI

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The challenge of large scale visual interaction Last decade has proven the superiority of models learned from data vs. hand engineered structures!

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Unsupervised: Learn models from found data; often exploit multiple modalities (text+image) Large-scale learning … The Tote is the perfect example of two handbag design principles that... The lines of this tote are incredibly sleek, but... The semi buckles that form the handle attachments are...

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E.g., finding visual senses 4 Artifact sense: telephone DICTIONARY 1: (n) telephone, phone, telephone set (electronic equipment that converts sound into electrical signals that can be transmitted over distances and then converts received signals back into sounds):phone telephone set 2: (n) telephone, telephony (transmitting speech at a distance): telephony [Saenko and Darrell 09]

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Unsupervised: Learn models from found data; often exploit multiple modalities (text+image) Supervised: Crowdsource labels (e.g., ImageNet) Large-scale Learning … The Tote is the perfect example of two handbag design principles that... The lines of this tote are incredibly sleek, but... The semi buckles that form the handle attachments are...

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Yet… Even the best collection of images from the web and strong machine learning methods can often yield poor classifiers on in-situ data! Supervised learning assumption: training distribution == test distribution Unsupervised learning assumption: joint distribution is stationary w.r.t. online world and real world Almost never true! 6 ?

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What You Saw Is Not What You Get The models fail due to domain shift SVM:54% NBNN:61% SVM:20% NBNN:19%

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Close-up Far-away amazon.com Consumer images FLICKR CCTV Examples of visual domain shifts digital SLRwebcam

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Examples of domain shift: change in camera, feature type, dimension digital SLR webcam SURF VQ to 300 SIFT VQ to 1000 Different dimensions

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Solutions? Do nothing (poor performance) Collect all types of data (impossible) Find out what changed (impractical) Learn what changed

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Prior Work on Domain Adaptation Pre-process the data [Daumé 07] : replicate features to also create source- and domain- specific versions; re-train learner on new features SVM-based methods [Yang07], [Jiang08], [Duan09], [Duan10] : adapt SVM parameters Kernel mean matching [Gretton09] : re-weight training data to match test data distribution

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Our paradigm: Transform-based Domain Adaptation Previous methods drawbacks cannot transfer learned shift to new categories cannot handle new features We can do both by learning domain transformations * Example: green and blue domains W * Saenko, Kulis, Fritz, and Darrell. Adapting visual category models to new domains. ECCV, 2010

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Symmetric assumption fails! Limitations of symmetric transforms Saenko et al. ECCV10 used metric learning: symmetric transforms same features How do we learn more general shifts ? W

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Asymmetric transform (rotation) Latest approach*: asymmetric transforms Metric learning model no longer applicable We propose to learn asymmetric transforms – Map from target to source – Handle different dimensions *Kulis, Saenko, and Darrell, What You Saw is Not What You Get: Domain Adaptation Using Asymmetric Kernel Transforms, CVPR 2011

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Asymmetric transform (rotation) W Latest approach: asymmetric transforms Metric learning model no longer applicable We propose to learn asymmetric transforms – Map from target to source – Handle different dimensions

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Model Details Learn a linear transformation to map points from one domain to another – Call this transformation W – Matrices of source and target: W

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Loss Functions Choose a point x from the source and y from the target, and consider inner product: Should be large for similar objects and small for dissimilar objects

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Loss Functions Input to problem includes a collection of m loss functions General assumption: loss functions depend on data only through inner product matrix

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Regularized Objective Function Minimize a linear combination of sum of loss functions and a regularizer: We use squared Frobenius norm as a regularizer – Not restricted to this choice

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The Model Has Drawbacks A linear transformation may be insufficient Cost of optimization grows as the product of the dimensionalities of the source and target data What to do?

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Kernelization Main idea: run in kernel space – Use a non-linear kernel function (e.g., RBF kernel) to learn non-linear transformations in input space – Resulting optimization is independent of input dimensionality – Additional assumption necessary: regularizer is a spectral function

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Kernelization Original Transformation Learning Problem Kernel matrices for source and target New Kernel Problem Relationship between original and new problems at optimality

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Summary of approach Input space 1. Multi-Domain Data 2. Generate Constraints, Learn W 3. Map via W4. Apply to New Categories Test point y1y1 y2y2

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Multi-domain dataset

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Experimental Setup Utilized a standard bag-of-words model Also utilize different features in the target domain – SURF vs SIFT – Different visual word dictionaries Baseline for comparing such data: KCCA

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Same-Category Results Baselines (knn, svm, metric learning) explained in paper Our Method

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Novel-class experiments Test methods ability to transfer domain shift to unseen classes Train transform on half of the classes, test on the other half Our Method (linear) Our Method

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Extreme shift example Nearest neighbors in source using transformation Query from target Nearest neighbors in source using KCCA+KNN

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Conclusion Should not rely on hand-engineered features any more than we rely on hand engineered models! Learn feature transformation across domains Developed a domain adaptation method based on regularized non-linear transforms – Asymmetric transform achieves best results on more extreme shifts – Saenko et al ECCV 2010 and Kulis et al CVPR 2011; journal version forthcoming

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