Pooling

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  1. We use three main types of layers to build ConvNet architectures: Convolutional Layer, Pooling Layer, and Fully-Connected Layer (exactly as seen in regular Neural Networks).[1]
  2. It is common to periodically insert a Pooling layer in-between successive Conv layers in a ConvNet architecture.[1]
  3. The Pooling Layer operates independently on every depth slice of the input and resizes it spatially, using the MAX operation.[1]
  4. A pooling layer with \(F = 3, S = 2\) (also called overlapping pooling), and more commonly \(F = 2, S = 2\).[1]
  5. For this reason, the selection of a correct pooling layer improves the final classification of the model.[2]
  6. The max function is the most common choice for the pooling layer in CNN architectures.[2]
  7. The attentive pooling is a neural attention mechanism which focuses on the relevant words, capturing the important semantic information without using lexical resources or NLP tools.[2]
  8. Firstly, we reduced the vector z d randomly dropping some of the elements of z (z* in the attentive pooling) with a probability p given by a Bernoulli distribution.[2]
  9. If one chooses the pooling regions to be contiguous areas in the image and only pools features generated from the same (replicated) hidden units.[3]
  10. Then, these pooling units will then be ”‘translation invariant”’.[3]
  11. However, unlike the cross-correlation computation of the inputs and kernels in the convolutional layer, the pooling layer contains no parameters (there is no kernel).[4]
  12. Instead, pooling operators are deterministic, typically calculating either the maximum or the average value of the elements in the pooling window.[4]
  13. These operations are called maximum pooling (max pooling for short) and average pooling, respectively.[4]
  14. At each location that the pooling window hits, it computes the maximum or average value of the input subtensor in the window, depending on whether max or average pooling is employed.[4]
  15. Pooling layers provide an approach to down sampling feature maps by summarizing the presence of features in patches of the feature map.[5]
  16. A pooling layer is a new layer added after the convolutional layer.[5]
  17. Pooling involves selecting a pooling operation, much like a filter to be applied to feature maps.[5]
  18. The pooling operation is specified, rather than learned.[5]
  19. The network architecture was a CNN consisting of four convolutional layers with 15 (13 × 13), 25 (9 × 9), 60 (7 × 7), and 130 (3 × 3) filters, with no pooling, and a final logistic regression layer.[6]
  20. The pooling is crucial component placed after the convolution layer.[7]
  21. The elementary pooling process involves down sampling of feature map by piercing into subregions.[7]
  22. This piercing and down sampling is defined by the pooling hyperparameters, viz.[7]
  23. The generally used global feature selection methods are average and max pooling.[7]
  24. Under the pooling of interests method , a business combination is regarded as the uniting of the ownership interests of two companies , not as the acquisition of one company by another.[8]
  25. Pooling is a feature commonly imbibed into Convolutional Neural Network (CNN) architectures.[9]
  26. The main idea behind a pooling layer is to “accumulate” features from maps generated by convolving a filter over an image.[9]
  27. Max pooling is done to in part to help over-fitting by providing an abstracted form of the representation.[9]
  28. Max pooling is done by applying a max filter to (usually) non-overlapping subregions of the initial representation.[9]
  29. Pooling is also useful when the latency is a concern, because a pool offers predictable times required to obtain resources since they have already been acquired.[10]
  30. Pooling samples involves mixing several samples together in a "batch" or pooled sample, then testing the pooled sample with a diagnostic test.[11]
  31. The FDA believes that sample pooling can be authorized for use in certain SARS-CoV-2 tests with appropriate mitigations and validation.[11]
  32. Generally, the FDA recommends validating the test with either pooling approach in a way that preserves the sensitivity of the test as much as possible.[11]
  33. Therefore, the FDA generally recommends that, after pooling, test performance includes ≥85% percent positive agreement (PPA) when compared with the same test performed on individual samples.[11]
  34. While the multinational pooling concept has been around for over 50 years, interest in this global funding mechanism remains as strong as ever.[12]
  35. From the ability to gather existing plan information to the tracking of new plan benefits or changes, pooling’s ancillary features are starting to become just as important as its financial savings.[12]
  36. To complement the increased flow of information pooling provides many companies are investing in technologies that enable them to access and maintain this data on an on-going basis.[12]
  37. The pooling networks as well as the major consulting firms have developed electronic databases for this purpose.[12]
  38. Creating ConvNets often goes hand in hand with pooling layers.[13]
  39. More specifically, we often see additional layers like max pooling, average pooling and global pooling.[13]
  40. Firstly, we’ll take a look at pooling operations from a conceptual level.[13]
  41. We explore the inner workings of a ConvNet and through this analysis show how pooling layers may help the spatial hierarchy generated in those models.[13]
  42. Global Average Pooling is a pooling operation designed to replace fully connected layers in classical CNNs.[14]
  43. One advantage of global average pooling over the fully connected layers is that it is more native to the convolution structure by enforcing correspondences between feature maps and categories.[14]
  44. CNNs usually consist of several basic units like convolutional unit, pooling unit, activation unit, and so on.[15]
  45. In CNNs, conventional pooling methods refer to 2×2 max-pooling and average-pooling, which are applied after the convolutional or ReLU layers.[15]
  46. In this paper, we propose a Multiactivation Pooling (MAP) Method to make the CNNs more accurate on classification tasks without increasing depth and trainable parameters.[15]
  47. We add more convolutional layers before one pooling layer and expand the pooling region to 4×4, 8×8, 16×16, and even larger.[15]
  48. The purpose of pooling is to spread financial risk across the population so that no individual carries the full burden of paying for health care.[16]
  49. WHO’s health financing team works with countries to design and implement health financing policies, including policies on pooling.[16]
  50. It's inevitable that ride-sharing and car-pooling services will become more common in Europe, but it is just going to take longer than elsewhere.[17]
  51. Pooling layers are used to reduce the dimensions of the feature maps.[18]
  52. The pooling layer summarises the features present in a region of the feature map generated by a convolution layer.[18]
  53. Max Pooling Max pooling is a pooling operation that selects the maximum element from the region of the feature map covered by the filter.[18]
  54. Further, it can be either global max pooling or global average pooling.[18]
  55. In this post, we’re going to discuss what max pooling is in a convolutional neural network.[19]
  56. We’re going to start out by explaining what max pooling is, and we’ll show how it’s calculated by looking at some examples.[19]
  57. We’ll then discuss the motivation for why max pooling is used, and we’ll see how we can add max pooling to a convolutional neural network in code using Keras.[19]
  58. Introducing max pooling Max pooling is a type of operation that is typically added to CNNs following individual convolutional layers.[19]
  59. Pooling of interests refers to a technique of recording a merger or acquisition, whereby the assets and liabilities of the two companies are summed together and then netted.[20]
  60. Before the discontinuation of the pooling of interests method, there were certain sectors that preferred the technique to the purchase price one.[20]
  61. A vast majority of technology firms used the pooling of interests technique so that they didn’t need to record any expenses arising from the acquisition.[20]
  62. As already mentioned, FASB, the organization that establishes and interprets generally accepted accounting principles, abolished the use of the pooling of interests method in 2001.[20]
  63. Therefore, as a new way to downsampling the features, we consider replacing the deterministic pooling function with a stochastic scheme.[21]
  64. In the pooling region, max pooling only considers the maximum element and ignores others.[21]
  65. In some cases, it will lead to an unacceptable result because if the most elements consist of high magnitudes, the distinguishing feature vanishes after max pooling.[21]
  66. On the average pooling function, | R i j | represents the size of the pooling region R i j .[21]
  67. Pooling allows laboratories to test more samples with fewer testing materials.[22]
  68. A pooling strategy depends on the community prevalence of virus, and pool size will need to be adjusted accordingly.[22]
  69. in vitro diagnostic device authorized by FDA for use with specimen pooling will be included on FDA’s list of In Vitro Diagnostics EUAsexternal icon.[22]
  70. The test report given to the individuals in the pool should indicate that the testing procedure involved specimen pooling and explain the limitations of that type of testing.[22]
  71. Pooling gives you some amount of translation invariance, which may or may not be helpful.[23]
  72. Also, pooling is faster to compute than convolutions.[23]

소스

  1. 1.0 1.1 1.2 1.3 CS231n Convolutional Neural Networks for Visual Recognition
  2. 2.0 2.1 2.2 2.3 Evaluation of pooling operations in convolutional architectures for drug-drug interaction extraction
  3. 3.0 3.1 Unsupervised Feature Learning and Deep Learning Tutorial
  4. 4.0 4.1 4.2 4.3 6.5. Pooling — Dive into Deep Learning 0.15.1 documentation
  5. 5.0 5.1 5.2 5.3 A Gentle Introduction to Pooling Layers for Convolutional Neural Networks
  6. Average Pooling - an overview
  7. 7.0 7.1 7.2 7.3 Interpretation of intelligence in CNN-pooling processes: a methodological survey
  8. meaning in the Cambridge English Dictionary
  9. 9.0 9.1 9.2 9.3 Max Pooling
  10. Pool (computer science)
  11. 11.0 11.1 11.2 11.3 Pooled Sample Testing and Screening Testing for COVID-19
  12. 12.0 12.1 12.2 12.3 The power of pooling resources
  13. 13.0 13.1 13.2 13.3 What are Max Pooling, Average Pooling, Global Max Pooling and Global Average Pooling? – MachineCurve
  14. 14.0 14.1 Global Average Pooling Explained
  15. 15.0 15.1 15.2 15.3 Multiactivation Pooling Method in Convolutional Neural Networks for Image Recognition
  16. 16.0 16.1 Pooling revenues and reducing fragmentation
  17. What does pooling mean?
  18. 18.0 18.1 18.2 18.3 Introduction to Pooling Layer - GeeksforGeeks
  19. 19.0 19.1 19.2 19.3 Max Pooling in Convolutional Neural Networks explained
  20. 20.0 20.1 20.2 20.3 Pooling of Interests
  21. 21.0 21.1 21.2 21.3 RunPool: A Dynamic Pooling Layer for Convolution Neural Network
  22. 22.0 22.1 22.2 22.3 Interim Guidance for Use of Pooling Procedures in SARS-CoV-2 Diagnostic, Screening, and Surveillance Testing
  23. 23.0 23.1 Why is max pooling necessary in convolutional neural networks?

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