Learning-To-Rank
State-of-the-art leaderboards
Greatest papers with code
Unsupervised Cross-dataset Person Re-identification by Transfer Learning of Spatial-Temporal Patterns
CVPR 2018
• ahangchen/TFusion
• 
Most of the proposed person re-identification algorithms conduct supervised training and testing on single labeled datasets with small size, so directly deploying these trained models to a large-scale real-world camera network may lead to poor performance due to underfitting.
LEARNING-TO-RANK TRANSFER LEARNING UNSUPERVISED PERSON RE-IDENTIFICATION
Exploiting Unlabeled Data in CNNs by Self-supervised Learning to Rank
Our results show that networks trained to regress to the ground truth targets for labeled data and to simultaneously learn to rank unlabeled data obtain significantly better, state-of-the-art results for both IQA and crowd counting.
ACTIVE LEARNING CROWD COUNTING IMAGE QUALITY ASSESSMENT LEARNING-TO-RANK
End-to-End Neural Ad-hoc Ranking with Kernel Pooling
• AdeDZY/K-NRM
•
Given a query and a set of documents, K-NRM uses a translation matrix that models word-level similarities via word embeddings, a new kernel-pooling technique that uses kernels to extract multi-level soft match features, and a learning-to-rank layer that combines those features into the final ranking score.
AD-HOC INFORMATION RETRIEVAL DOCUMENT RANKING LEARNING-TO-RANK WORD EMBEDDINGS
Learning Latent Vector Spaces for Product Search
We introduce a novel latent vector space model that jointly learns the latent representations of words, e-commerce products and a mapping between the two without the need for explicit annotations.
Leveraging Unlabeled Data for Crowd Counting by Learning to Rank
CVPR 2018 • xialeiliu/CrowdCountingCVPR18
We propose a novel crowd counting approach that leverages abundantly available unlabeled crowd imagery in a learning-to-rank framework.
PRUNE: Preserving Proximity and Global Ranking for Network Embedding
NeurIPS 2017
• ntumslab/PRUNE
•
We investigate an unsupervised generative approach for network embedding.
COMMUNITY DETECTION LEARNING-TO-RANK LINK PREDICTION NETWORK EMBEDDING
Using Titles vs. Full-text as Source for Automated Semantic Document Annotation
For the first time, we offer a systematic comparison of classification approaches to investigate how far semantic annotations can be conducted using just the metadata of the documents such as titles published as labels on the Linked Open Data cloud.
Ranking Distillation: Learning Compact Ranking Models With High Performance for Recommender System
We propose a KD technique for learning to rank problems, called \emph{ranking distillation (RD)}.
Modeling Label Ambiguity for Neural List-Wise Learning to Rank
List-wise learning to rank methods are considered to be the state-of-the-art.
Hashing as Tie-Aware Learning to Rank
Hashing, or learning binary embeddings of data, is frequently used in nearest neighbor retrieval.