Yet, ImageNet is now nearly ten years old and is by modern standards "small". .. Exploring the Limits of Weakly Supervised Pretraining @article{Mahajan2018ExploringTL, title={Exploring the Limits of Weakly Supervised Pretraining}, author={Dhruv Kumar Mahajan and Ross B. Girshick and Vignesh Ramanathan and Kaiming He and Manohar Paluri and Yixuan Li and Ashwin Bharambe and Laurens van der Maaten}, … Privacy notice: By enabling the option above, your browser will contact the API of web.archive.org to check for archived content of web pages that are no longer available. Exploring the Limits of Weakly Supervised Pretraining 9 sistent across hashtag vocabulary sizes and models, the accuracy increase yis larger for higher-capacity networks: across all gures, the lines corresponding to ResNeXt-101 32 16d networks (purple) are steeper than those corresponding to 32 8d and 32 4d models. Typically, predictive models are learned from a training data set that contains a large amount of training examples, each corresponding to an event/object. Press J to jump to the feed. blog; statistics; browse. DOI: 10.1007/978-3-030-01216-8_12 Corpus ID: 13751202. In classification, the label indicates the class to which the training ex… Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. Yet, ImageNet is now nearly ten years old and is by modern standards small. the dblp computer science bibliography is funded by: Exploring the Limits of Weakly Supervised Pretraining. 37 Citations; 1.6k Downloads; Part of the Lecture Notes in Computer Science book series (LNCS, volume 11206) … Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Yet, ImageNet is now nearly ten years old and is by modern standards "small". [1706.02677] Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour, [1604.01325] Deep Image Retrieval: Learning global representations for image search, [1602.07261] Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning, [1707.07012] Learning Transferable Architectures for Scalable Image Recognition, [1711.11443] ConvNets and ImageNet Beyond Accuracy: Understanding Mistakes and Uncovering Biases, [1702.08734] Billion-scale similarity search with GPUs, [1610.02357] Xception: Deep Learning with Depthwise Separable Convolutions, [1611.05725] PolyNet: A Pursuit of Structural Diversity in Very Deep Networks, [1709.01507] Squeeze-and-Excitation Networks. Explore Explore GitHub → ... Trending; Learning Lab; Open source guides; Connect with others. Please also note that this feature is work in progress and that it is still far from being perfect. Add open access links from to the list of external document links (if available). team; license; privacy; imprint; manage site settings. State-of-the-art visual perception models for a wide range of tasks rely on supervised pretraining. Exploring the Limits of Weakly Supervised Pretraining . ImageNet classification is the defacto pretraining task for these models. JavaScript is requires in order to retrieve and display any references and citations for this record. Exploring the Limits of Weakly Supervised Pretraining Dhruv Mahajan, Ross Girshick, Vignesh Ramanathan, Kaiming He, Manohar Paluri, Yixuan Li, Ashwin Bharambe, Laurens van der Maaten (Submitted on 2 May 2018) State-of-the-art visual perception models for a wide range of tasks rely on supervised pretraining. ImageNet classification is the de facto pretraining task for these models. Dhruv Mahajan, Ross Girshick, Vignesh Ramanathan, Kaiming He, Manohar Paluri, Yixuan Li, Ashwin Bharambe, Laurens van der Maaten; Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. For web page which are no longer available, try to retrieve content from the of the Internet Archive (if available). Authors; Authors and affiliations; Dhruv Mahajan; Ross Girshick; Vignesh Ramanathan; Kaiming He; Manohar Paluri; Yixuan Li; Ashwin Bharambe ; Laurens van der Maaten; Conference paper. f.a.q.
If citation data of your publications is not openly available yet, then please consider asking your publisher to release your citation data to the public. So please proceed with care and consider checking the Internet Archive privacy policy. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar. You need to opt-in for them to become active. last updated on 2020-09-28 08:19 CEST by the dblp team, all metadata released as open data under CC0 1.0 license, see also: Terms of Use | Privacy Policy | Imprint. ECCV (2) 2018: 185-201. home. Exploring the Limits of Weakly Supervised Pretraining. By Dhruv Mahajan, Ross Girshick, Vignesh Ramanathan, Kaiming He, Manohar Paluri, Yixuan Li, Ashwin Bharambe and Laurens van der Maaten. Recently hyped ML content linked in one simple page, Sources: reddit/r/{MachineLearning,datasets}, arxiv-sanity, twitter, kaggle/kernels, hackernews, awesome-datasets, sota changes, // Enable JavaScript to see more content, [1805.00932] Exploring the Limits of Weakly Supervised Pretraining, For instance, we find that the accuracy difference between models pretrained using two different vocabularies increases as the number of target classes increases (Figure ?? Exploring the Limits of Weakly Supervised Pretraining 05/02/2018 ∙ by Dhruv Mahajan, et al. A training example consists of two parts: a feature vector (or instance) describing the event/object, and a label indicating the ground-truth output. Add a list of references from , , and to record detail pages. ImageNet classification is the de facto pretraining task for these models. [1608.08614] What makes ImageNet good for transfer learning? So please proceed with care and consider checking the Unpaywall privacy policy. blog; statistics; browse. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar. Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. To protect your privacy, all features that rely on external API calls from your browser are turned off by default.
Yet, ImageNet is … All settings here will be stored as cookies with your web browser. State-of-the-art visual perception models for a wide range of tasks rely on supervised pretraining. At the same time, Twitter will persitently store several cookies with your web browser. While we did signal Twitter to not track our users by setting the "dnt" flag, we do not have any control over how Twitter uses your data. @inproceedings{wslimageseccv2018, title={Exploring the Limits of Weakly Supervised Pretraining}, author={Dhruv Kumar Mahajan and Ross B. Girshick and Vignesh Ramanathan and Kaiming He and Manohar Paluri and Yixuan Li and Ashwin Bharambe and Laurens van der Maaten}, booktitle={ECCV}, year={2018} } License Machine learning has achieved great success in various tasks, particularly in supervised learning tasks such as classification and regression. State-of-the-art visual perception models for a wide range of tasks rely on supervised pretraining. Our experiments demonstrate that training for large-scale hashtag prediction leads to excellent results. ∙ 0 ∙ share State-of-the-art visual perception models for a wide range of tasks rely on supervised pretraining. Press question mark to learn the rest of the keyboard shortcuts For more information see our F.A.Q. load references from crossref.org and opencitations.net. State-of-the-art visual perception models for a wide range of tasks rely on supervised pretraining. Get PDF (985 KB) Abstract.
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First Online: 09 October 2018. ImageNet classification is the de facto pretraining task for these models. For more information please see the Initiative for Open Citations (I4OC). To protect your privacy, all features that rely on external API calls from your browser are turned off by default. ImageNet classification is the de facto pretraining task for these models. Even so, relatively little is known about the behavior of pretraining with datasets that are multiple orders of magnitude larger. search dblp; lookup by ID; about. Please note: Providing information about references and citations is only possible thanks to to the open metadata APIs provided by crossref.org and opencitations.net. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. That is, in particular. Weakly supervised learning is an umbrella covering a va- riety of studies which attempt to construct predictive mod- els by learning with weak supervision. Please also note that there is no way of submitting missing references or citation data directly to dblp. Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. we do not have complete and curated metadata for all items given in these lists. Exploring the Limits of Weakly Supervised Pretraining Dhruv Mahajan Manohar Paluri Ross Girshick Vignesh Ramanathan Kaiming He Yixuan Li Ashwin Bharambe Laurens van der Maaten Facebook Abstract. 181-196 Abstract.
Add a list of citing articles from and to record detail pages. Exploring the Limits of Weakly Supervised Pretraining. CoRR abs/1805.00932 (2018) home. ): if we would have only evaluated our models on ImageNet-1k, we would have concluded they learned visual features of similar quality, whereas results on ImageNet-9k show that one model learns substantially better features than the other, [1707.02968] Revisiting Unreasonable Effectiveness of Data in Deep Learning Era, [1905.00546] Billion-scale semi-supervised learning for image classification, [1905.00546v1] Billion-scale semi-supervised learning for image classification, [1708.06734] Representation Learning by Learning to Count, [1807.05520] Deep Clustering for Unsupervised Learning of Visual Features, [1710.06924] VisDA: The Visual Domain Adaptation Challenge, [1406.5774] Factors of Transferability for a Generic ConvNet Representation, [1905.01278] Leveraging Large-Scale Uncurated Data for Unsupervised Pre-training of Visual Features, [1811.08737] SpotTune: Transfer Learning through Adaptive Fine-tuning, [1806.09755] Syn2Real: A New Benchmark forSynthetic-to-Real Visual Domain Adaptation, [1310.1531] DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition. f.a.q.
Exploring the Limits of Weakly Supervised Pretraining.
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In this paper, we present a unique study of transfer learning with large convolutional networks trained to predict hashtags on billions of social media images. State-of-the-art visual perception models for a wide range of tasks rely on supervised pretraining. the lists below may be incomplete due to unavailable citation data, reference strings may not have been successfully mapped to the items listed in dblp, and. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data.
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