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Greatest papers with code

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 SELF-SUPERVISED LEARNING

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.

INCREMENTAL LEARNING LEARNING-TO-RANK TRANSFER LEARNING UNSUPERVISED PERSON RE-IDENTIFICATION

NeuralNDCG: Direct Optimisation of a Ranking Metric via Differentiable Relaxation of Sorting

15 Feb 2021allegro/allRank

As a result, we obtain a new ranking loss function which is an arbitrarily accurate approximation to the evaluation metric, thus closing the gap between the training and the evaluation of LTR models.

INFORMATION RETRIEVAL LEARNING-TO-RANK

Context-Aware Learning to Rank with Self-Attention

20 May 2020allegro/allRank

In learning to rank, one is interested in optimising the global ordering of a list of items according to their utility for users. Popular approaches learn a scoring function that scores items individually (i. e. without the context of other items in the list) by optimising a pointwise, pairwise or listwise loss.

LEARNING-TO-RANK

PT-Ranking: A Benchmarking Platform for Neural Learning-to-Rank

31 Aug 2020wildltr/ptranking

In this work, we propose PT-Ranking, an open-source project based on PyTorch for developing and evaluating learning-to-rank methods using deep neural networks as the basis to construct a scoring function.

LEARNING-TO-RANK

Introducing LETOR 4.0 Datasets

9 Jun 2013wildltr/ptranking

We call the two query sets MQ2007 and MQ2008 for short.

LEARNING-TO-RANK

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

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.

DOCUMENT RANKING LEARNING-TO-RANK WORD EMBEDDINGS

SetRank: Learning a Permutation-Invariant Ranking Model for Information Retrieval

12 Dec 2019ULTR-Community/ULTRA

In learning-to-rank for information retrieval, a ranking model is automatically learned from the data and then utilized to rank the sets of retrieved documents.

INFORMATION RETRIEVAL LEARNING-TO-RANK