CNN-弱监督-迁移学习-exploring_the_limits_of_weakly_supervised_pretraini
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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
Facebook
Abstract. 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. Yet, ImageNet is now nearly ten
years old and is by modern standards “small”. Even so, relatively little is
known about the behavior of pretraining with datasets that are multiple
orders of magnitude larger. The reasons are obvious: such datasets are
difficult to collect and annotate. 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. Our experiments demon-
strate that training for large-scale hashtag prediction leads to excellent
results. We show improvements on several image classification and object
detection tasks, and report the highest ImageNet-1k single-crop, top-1
accuracy to date: 85.4% (97.6% top-5). We also perform extensive ex-
periments that provide novel empirical data on the relationship between
large-scale pretraining and transfer learning performance.
1 Introduction
Nearly all state-of-the-art visual perception algorithms rely on the same formula:
(1) pretrain a convolutional network on a large, manually annotated image clas-
sification dataset and (2) finetune the network on a smaller, task-specific dataset.
This formula [1,2,3] has been in wide use for several years and led to impres-
sive improvements on numerous tasks. Examples include: object detection [1,4],
semantic segmentation [5,6], human pose estimation [7,8], video recognition [9],
monocular depth estimation [10], and so on. In fact, it is so effective that it
would now be considered foolhardy not to use supervised pretraining.
The ImageNet dataset [11] is the de facto pretraining dataset. While there are
studies analyzing the effects of various ImageNet pretraining factors on transfer
learning (e.g., [12,13]) or the use of different datasets that are of the same size
magnitude as ImageNet (e.g., [14,15]), relatively little is known about pretraining
on datasets that are multiple orders of magnitude larger ([16,17] are the largest
studies to date). The reasons for this are numerous: few such datasets exist,
building new datasets is labor intensive, and large computational resources are
needed to conduct experiments. Yet, given the central role of pretraining it is
important to expand our scientific knowledge in this domain.
This paper tries to address this complex issue by studying an unexplored data
regime: billions of images “labeled” in the wild with social media hashtags. This
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