Direct Modeling of Complex Invariances for Visual Object Features

By Kaifa Hui. 2013

30 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Representation & Self-Supervised Learning

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Citations in the corpus

Discriminative Unsupervised Feature Learning with Convolutional Neural Networks (2014, neutral): “To each feature map, we applied the pooling method that is commonly used for the respective dataset: 1) 4-quadrant max-pooling, resulting in 4 values per feature map, which is the standard procedure for STL-10 and CIFAR-10 [26, 10, 27, 12]; 2) 3-layer spatial pyramid, i.”
Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks (2015, neutral): “34 0.71 Small-CNN (Rasmus et al., 2015) 6.43 ( ± 0.84) 0.36 Conv-Ladder Γ -model (Rasmus et al., 2015) Hui (2013) 27.4 ( ± 0.7) 18.1 Exemplar-CNN (Dosovitskiy et al., 2014) 23.4 ( ± 0.2) 15.7 Conv-Ladder Γ -model (Rasmus et al., 2015) We performed experiments using two setups: (1) using a subset of…”
Deep Growing Learning (2017, unknown): “, Spike-and-Slab Sparse Coding [8] and View-Invariant k-means [16].”
Do More Dropouts in Pool5 Feature Maps for Better Object Detection (2014, unknown): “One kind approach is to manipulate the training images by different operations, such as rotation, scaling, shearing and stretching, and then merge these transformed images into training set for training a more powerful detector which will improve the view-invariant representations [20].”
Deep unsupervised learning through spatial contrasting (2016, methodology): “For Cifar10 dataset, we use the same setting as Coates & Ng (2012) and Hui (2013) to test a model’s ability to learn from unlabeled images.”
Ieee Transactions on Pattern Analysis and Machine Intelligence 1 Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks (2014, unknown): “This is in agreement with previous evidence that with increasing feature vector dimensionality and number of labeled samples , training an SVM becomes less dependent on the quality of the features [16], [33].”
Unsupervised Learning of Discriminative Attributes and Visual Representations (2016, background): “Many unsupervised studies [4, 5, 6, 24, 53] focus on learning generic visual features by modeling the data distribution (e.”