Learning Similarity Metrics with Invariance Properties

author: Yann LeCun, Computer Science Department, New York University
published: Feb. 25, 2007,   recorded: December 2006,   views: 656
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Slides

Slides
0:03 Learning Similarity Metrics with Invariances
0:21 Learning an Invariant Dissimilarity Metric
2:36 Dissimilarity Metric for Face Recognition
3:54 Siamese Architecture
4:50 Dissimilarity Metric vs Traditional Classification
5:10 Trainable Metric vs Other Dimensionality Reduction Methods
7:17 Trainable Metrics vs handcrafted invariances
7:49 Siamese Architecture for Comparing TimeSeries Data
8:41 1D Convolutional Net (TDNN)
8:44 Examples
8:55 Siamese Architecture
9:09 Probabilistic Training: Maximum Likelihood
11:07 Solution?
12:23 Another Loss Function
15:31 Loss Function
16:58 Examples of Loss Functions
17:26 Loss Function: SquareExponential
17:37 Face Verification datasets: AT&T, FERET, and AR/Purdue
19:04 Face Verification datasets: AT&T, FERET, and AR/Purdue
19:25 Face Verification datasets: AT&T, FERET, and AR/Purdue
19:57 Face Verification dataset: AR/Purdue
20:04 Preprocessing
20:17 Centering with a Gaussianblurred face template
20:36 Alternated Convolutions and Subsampling
20:41 Architecture for the Mapping Function Gw(X)
21:02 Internal state for genuine and impostor pairs
22:25 Gaussian Face Model in the output space
23:05 Dataset for Verification - Verification Results
23:50 Classification Examples
25:07 lInternal State
25:29 DrLim: Dimensionality; Reduction by Learning an Invariant Mapping
32:10 “Traditional” Manifold Learning
33:17 Learning a FUNCTION from input to output
33:24 Learning an INVARIANT FUNCTION from input to output
34:32 Learning Invariant Manifolds with EBMs
35:29 Step 1: Building a Neighborhood Graph
36:12 Step 2: Pick a Parameterized Family of Function
36:17 Step 3: Pick a Loss function and Minimize it w.r.t. W
36:31 Architecture
36:35 Architecture and loss function
37:08 Loss function
37:34 Mechanical Analogy
37:54 MNIST Dataset
38:01 MNIST Handwritten Digits. Sanity Check
39:01 Architecture of the Gw(X) Function:
39:04 Alternated Convolutions and Subsampling
39:06 Learning a mapping that is invariant to shifts
39:27 Simple Experiment with Shifted MNIST
39:42 Shifted MNIST: LLE Result
41:19 Shifted MNIST: Injecting Prior Knowledge
42:35 Discovering the Viewpoint Manifold
44:14 Generic Object Detection and Recognition with Invariance to Pose and Illumination
44:17 Data Collection, Sample Generation
44:19 NORB Dataset: LLE
45:22 Automatic Discovery of the Viewpoint Manifold with Invariant to Illumination
46:56 NORB Dataset: Learned Hidden Units

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