← Core Methods
Machine Learning › Core Methods ›

Metric Learning

2800 directly classified papers

Papers per year

Papers

Unsupervised Feature Selection by Preserving Stochastic Neighbors AISTATS 2016
Structured Feature Similarity With Explicit Feature Map CVPR 2016
Efficient Temporal Sequence Comparison and Classification Using Gram Matrix Embeddings on a Riemannian Manifold CVPR 2016
Identifying Perceptually Similar Voices with a Speaker Recognition System Using Auto-Phonetic Features INTERSPEECH 2016
Individual Identity in Songbirds: Signal Representations and Metric Learning for Locating the Information in Complex Corvid Calls INTERSPEECH 2016
The Multiscale Laplacian Graph Kernel NIPS 2016
Learning Local Image Descriptors With Deep Siamese and Triplet Convolutional Networks by Minimising Global Loss Functions CVPR 2016
Disentrainment may be a Positive Thing: A Novel Measure of Unsigned Acoustic-Prosodic Synchrony, and its Relation to Speaker Engagement INTERSPEECH 2016
Learning a Discriminative Null Space for Person Re-Identification CVPR 2016
Learning Kernels with Random Features NIPS 2016
What Makes Objects Similar: A Unified Multi-Metric Learning Approach NIPS 2016
Latent Embeddings for Zero-Shot Classification CVPR 2016
Deep Relative Distance Learning: Tell the Difference Between Similar Vehicles CVPR 2016
Accumulated Stability Voting: A Robust Descriptor From Descriptors of Multiple Scales CVPR 2016
Fine-Grained Categorization and Dataset Bootstrapping Using Deep Metric Learning With Humans in the Loop CVPR 2016
Nearly Isometric Embedding by Relaxation NIPS 2016
Learning Deep Structure-Preserving Image-Text Embeddings CVPR 2016
Deep Neural Networks with Inexact Matching for Person Re-Identification NIPS 2016
Learning to Assign Orientations to Feature Points CVPR 2016
Approximate Log-Hilbert-Schmidt Distances Between Covariance Operators for Image Classification CVPR 2016
When VLAD Met Hilbert CVPR 2016
Linear Relaxations for Finding Diverse Elements in Metric Spaces NIPS 2016
Improved Error Bounds for Tree Representations of Metric Spaces NIPS 2016
Learning Deep Embeddings with Histogram Loss NIPS 2016
Sample Complexity of Learning Mahalanobis Distance Metrics NIPS 2015