  Deep learning has shown remarkable progress in a wide range of problems.
However, efficient training of such models requires large-scale datasets, and
getting annotations for such datasets can be challenging and costly. In this
work, we explore the use of user-generated freely available labels from web
videos for video understanding. We create a benchmark dataset consisting of
around 2 million videos with associated user-generated annotations and other
meta information. We utilize the collected dataset for action classification
and demonstrate its usefulness with existing small-scale annotated datasets,
UCF101 and HMDB51. We study different loss functions and two pretraining
strategies, simple and self-supervised learning. We also show how a network
pretrained on the proposed dataset can help against video corruption and label
noise in downstream datasets. We present this as a benchmark dataset in noisy
learning for video understanding. The dataset, code, and trained models will be
publicly available for future research.
