Smart Mining for Deep Metric Learning
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Cites methods from "Smart Mining for Deep Metric Learni..."
...These methods need careful treatment of negative pairs [13] by either relying on large batch sizes [8, 12], memory banks [9] or customized mining strategies [14, 15] to retrieve the negative pairs....
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Cites background or methods from "Smart Mining for Deep Metric Learni..."
...Following existing works on supervised deep embedding learning [13, 32], the retrieval performance and clustering quality of the testing set are evaluated....
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...Most of them are designed on top of pairwise [12, 30] or triplet relationships [13, 29]....
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...In particular, several sampling strategies are widely investigated to improve the performance, such as hard mining [16], semihard mining [35], smart mining [13] and so on....
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References
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"Smart Mining for Deep Metric Learni..." refers background in this paper
...Also in (1), note that fl = [fl,1, ..., fl,nl ] represents an array of nl pre-activation functions....
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8,289 citations
"Smart Mining for Deep Metric Learni..." refers background or methods in this paper
...2, semi-hard mining has proved an effective method for training triplet networks [14] with the primary aim of finding sets of triplets that will continue to progress the training of the network....
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...(5) In order to avoid the costly argmax over the entire training set, semi-hard mining is instead commonly performed over the stochastic subset of samples used in each minibatch [16, 14]....
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...The development of deep metric learning models for the estimation of effective feature embedding [2, 4, 9, 11, 15, 16, 17, 13, 22, 25, 27, 26] is at the core of many recently proposed computer vision methods [3, 14, 19, 24, 28]....
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...This issue has lead to the implementation of importance sampling techniques [14, 16, 24] that stochastically under-samples the set of triplets....
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...Similar to [14, 9], we set the margin for the triplet and global loss to 0....
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"Smart Mining for Deep Metric Learni..." refers background or methods in this paper
...ll@K metric [15]. Tables1and2show the NMI and k nearest neighbour performance with the Recall@K metric results defined above comparing our method to the state of the art for the datasets CUB- 200-2011 [25] and Cars196 [9]. From these tables, we can first see that Triplet + FANNG significantly improves upon the Semi-hard [16] results with respect to all measures, and showing that the smart mining process ...
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...final model Triplet + FANNG + Global + Adaptive shows competitive results with respect to all measures as well as a much faster convergence rate (see Fig.4). For instance, for the CUB-200-2011 dataset [25], Triplet + FANNG + Global + Adaptive converges in just Table 1. Clustering and recall performance on CUB-200- 2011 [25]. Our proposals are highlighted. Method NMI R@1 R@2 R@4 R@8 Semi-hard [16] 55.38...
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...dings in far Figure 3. A comparison of training performance using hand tuned and adaptive selection of . Training and validation error is shown for the first 20 epochs while training on CUB- 200-2011 [25]. fewer epochs. To maintain a high training error, it is best to use batches that are 50% to 100% mined triplets. A comparison of hand tuned and adaptive parameter selection can be seen in figure3. Tra...
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...r epochs will greatly reduce the overall training time. 4. Experiments For the experiments, we follow the protocol used in previous papers [20,21,15], which uses unseen classes from the CUB- 200-2011 [25] and Cars196 [9] datasets in order to assess the clustering quality and k nearest neighbour retrieval [8]. Our proposed method combining triplet and global losses using FANNG [5] with and without auto...
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...’s paper [15]. For the remaining approaches (i.e. our proposed method, and (5) ), we use the same training and test set split described in [21] across all datasets. Specifically, the means CUB200-2011 [25] has 11;788 images of 200 bird species, from which we take the first 100 species for training and use the remaining 100 species for testing. Cars196 [9] has 16;185 images from 196 car models, from whic...
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2,980 citations
"Smart Mining for Deep Metric Learni..." refers background in this paper
...Arguably, the most explored deep learning model that can estimate feature embedding is based on triplet networks [6, 24], which are an extension of the siamese network [1]....
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