Naive Bayes Nearest Neighbour (NBNN) is a simple and effective framework which addresses many of the pitfalls of K-Nearest Neighbour (KNN) classification. It has yielded competitive results on several computer vision benchmarks. Its central tenet is that during NN search, a query is not compared to every example in a database, ignoring class information. Instead, NN searches are performed within each class, generating a score per class. A key problem with NN techniques, including NBNN, is that they fail when the data representation does not capture perceptual (e.g. class-based) similarity. NBNN circumvents this by using independent engineered descriptors (e.g. SIFT). To extend its applicability outside of image-based domains, we propose to learn a metric which captures perceptual similarity. Similar to how Neighbourhood Components Analysis optimizes a differentiable form of KNN classification, we propose "Class Conditional" metric learning (CCML), which optimizes a soft form of the NBNN selection rule. Typical metric learning algorithms learn either a global or local metric. However, our proposed method can be adjusted to a particular level of locality by tuning a single parameter. An empirical evaluation on classification and retrieval tasks demonstrates that our proposed method clearly outperforms existing learned distance metrics across a variety of image and non-image datasets.
Salakhutdinov and Hinton extended NCA to the non-linear setting by incorporating a multi-layer neural network into the embedding and training it with backpropagation @cite_18 . They incorporated unsupervised pre-training @cite_31 into their formulation and regularized with an auto-encoder objective. In a similar manner, we demonstrate the extension of our method to the non-linear setting by utilizing an embedding based on convolutional neural networks.
@cite_18 @cite_31
205159212 2136922672
A dental model trimmer having an easily replaceable abrasive surfaced member. The abrasive surfaced member is contained within a housing and is releasably coupled onto a back plate assembly which is driven by a drive motor. The housing includes a releasably coupled cover plate providing access to the abrasive surfaced member. An opening formed in the cover plate exposes a portion of the abrasive surface so that a dental model workpiece can be inserted into the opening against the abrasive surface to permit work on the dental model workpiece. A tilting work table beneath the opening supports the workpiece during the operation. A stream of water is directed through the front cover onto the abrasive surface and is redirected against this surface by means of baffles positioned inside the cover plate. The opening includes a beveled boundary and an inwardly directed lip permitting angular manipulation of the workpiece, better visibility of the workpiece and maximum safety. We show how to use "complementary priors" to eliminate the explaining-away effects that make inference difficult in densely connected belief nets that have many hidden layers. Using complementary priors, we derive a fast, greedy algorithm that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory. The fast, greedy algorithm is used to initialize a slower learning procedure that fine-tunes the weights using a contrastive version of the wake-sleep algorithm. After fine-tuning, a network with three hidden layers forms a very good generative model of the joint distribution of handwritten digit images and their labels. This generative model gives better digit classification than the best discriminative learning algorithms. The low-dimensional manifolds on which the digits lie are modeled by long ravines in the free-energy landscape of the top-level associative memory, and it is easy to explore these ravines by using the directed connections to display what the associative memory has in mind.