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Learning-To-Rank

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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

17 Feb 2019xialeiliu/RankIQA

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

20 Jun 2017AdeDZY/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

Unbiased LambdaMART: An Unbiased Pairwise Learning-to-Rank Algorithm

16 Sep 2018acbull/Unbiased_LambdaMart

Although click data is widely used in search systems in practice, so far the inherent bias, most notably position bias, has prevented it from being used in training of a ranker for search, i. e., learning-to-rank.

LEARNING-TO-RANK

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.

CROWD COUNTING IMAGE RETRIEVAL LEARNING-TO-RANK

Learning Latent Vector Spaces for Product Search

25 Aug 2016cvangysel/SERT

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.

LEARNING-TO-RANK

Unbiased Learning to Rank with Unbiased Propensity Estimation

16 Apr 2018QingyaoAi/Dual-Learning-Algorithm-for-Unbiased-Learning-to-Rank

We find that the problem of estimating a propensity model from click data is a dual problem of unbiased learning to rank.

LEARNING-TO-RANK

Deep Metric Learning to Rank

CVPR 2019 kunhe/FastAP-metric-learning

We propose a novel deep metric learning method by revisiting the learning to rank approach.

IMAGE RETRIEVAL LEARNING-TO-RANK METRIC LEARNING QUANTIZATION

Ranking Distillation: Learning Compact Ranking Models With High Performance for Recommender System

19 Sep 2018graytowne/rank_distill

We propose a KD technique for learning to rank problems, called \emph{ranking distillation (RD)}.

LEARNING-TO-RANK RECOMMENDATION SYSTEMS