Large-Scale Deep Learning on the YFCC100M Dataset

By Karl S. Ni, Roger A. Pearce, Kofi Boakye, Brian Van Essen, Damian Borth, Barry Y. Chen, E. Wang. 2015

32 citations recorded by the source corpus.

We present a work-in-progress snapshot of learning with a 15 billion parameter deep learning network on HPC architectures applied to the largest publicly available natural image and video dataset released to-date. Recent advancements in unsupervised deep neural networks suggest that scaling up such networks in both model and training dataset size can yield significant improvements in the learning of concepts at the highest layers. We train our three-layer deep neural network on the Yahoo! Flickr Creative Commons 100M dataset. The dataset comprises approximately 99.2 million images and 800,000 user-created videos from Yahoo's Flickr image and video sharing platform. Training of our network takes eight days on 98 GPU nodes at the High Performance Computing Center at Lawrence Livermore National Laboratory. Encouraging preliminary results and future research directions are presented and discussed.

Subfields: Computer Vision and Pattern Recognition

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Citations in the corpus

Revisiting Unreasonable Effectiveness of Data in Deep Learning Era (2017, background): “In order to overcome the bottleneck, there have been recent efforts on visual representation learning using web-supervision [2, 5, 6, 9, 21, 23, 24, 27] or unsupervised [10, 11, 31, 32, 34, 42, 43] paradigms.”
EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification (2017, methodology): “We provide this paper in the context of the recently published EuroSAT dataset, which can be used similar to [18] as a basis for large-scale training of deep neural networks for the task of satellite image classification.”
Learning Visual Features from Large Weakly Supervised Data (2015, background): “Ni et al. [32] also train convolutional networks on tens of millions of image-word pairs, but their study focuses on systems issues and does not report recognition performances.”
Leveraging Large-Scale Uncurated Data for Unsupervised Pre-training of Visual Features (2019, methodology): “Some methods [9, 17, 32] aim at learning visual features from uncurated data streams.”
Unsupervised Pre-Training of Image Features on Non-Curated Data (2019, unknown): “Some methods [9, 17, 32] aim at learning visual features from non-curated data streams.”
Improving Shape Deformation in Unsupervised Image-to-Image Translation (2018, methodology): “Similar to Morsita et al. [31], we extracted 13,336 images from the Flickr100m dataset [33] using speciﬁc doll manufacturers Table 3.”
Real-time Analysis and Visualization of the YFCC100m Dataset (2015, background): “Its vast size allows not only the analysis of how user generated media content is shared and annotated, but also provides a richness of content to serves as a large-scale source for training statistical methods [11] or the creation of specialized subsets [1, 4] However, to make this dataset accessib”