Transformation invariance in pattern recognition: Tangent distance and propagation
By Patrice Y. Simard, Yann LeCun, John S. Denker, Bernard Victorri. 2000
160 citations recorded by the source corpus.
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Citations in the corpus
- Dropout Training as Adaptive Regularization (2013, background): “Dropout training falls into the broader category of learning methods that artificially corrupt training data to stabilize predictions [2, 4, 5, 6, 7].”
- Learning the Irreducible Representations of Commutative Lie Groups (2014, neutral): “In practice, it has proven difficult to compute this distance exactly, so approximations such as tangent distance have been invented (Simard et al., 2000).”
- Translation insensitive image similarity in complex wavelet domain (2005, background): “However, it has been shown that they perform poorly in image quality assessment and pattern recognition tasks (e.g., [1, 2 ]).”
- High-Dimensional Asymptotics of Prediction: Ridge Regression and Classification (2015, background): “…and Tao, 2007; Hastie et al., 2015), the “manifold hypothesis” positing that the x i have useful low-dimensional geometric structure (Rifai et al., 2011; Simard et al., 2000), and several variants of an “independence hypothesis” that rely on independence assumptions for the feature distribution…”
- Learning a similarity metric discriminatively, with application to face verification (2005, methodology): “One example is the Tangent Distance method [ 19 ].”
- Feature Noising for Log-Linear Structured Prediction (2013, methodology): “The effectiveness of this technique is well-known in machine learning (AbuMostafa, 1990; Burges and Schölkopf, 1997; Simard et al., 2000; Rifai et al., 2011a; van der Maaten et al., 2013), but working directly with many corrupted copies of a dataset can be computationally prohibitive.”
- Face Detection Based on Multi-Block LBP Representation (2007, background): “Due to the variations in facial appearance, lighting, expressions, and other factors [11], face/nonface classifiers with good performance should be very complex.”
- Illumination Invariant Face Recognition Using Near-Infrared Images (2007, background): “Extrinsic factors, including eyeglasses, hairstyle, expression, posture, and environmental lighting, which make distributions of face data highly complex [5], [6], [7], should be minimized for reliable face recognition.”
- Knowledge Transfer in Deep convolutional Neural Nets (2007, methodology): “In order to then incorporate this information, EBNN’s are trained using TangentProp (Simard et al. 2001).”