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data2vec: General … Self-supervised Learning in Speech, Vision and Language
JC – Journal Club
# data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language (2022)
Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli
This paper will be presented by:
While the general idea of self-supervised learning is identical across modalities, the actual algorithms and objectives differ widely because they were developed with a single modality in mind. To get us closer to general self-supervised learning, we present data2vec, a framework that uses the same learning method for either speech, NLP or computer vision. The core idea is to predict latent representations of the full input data based on a masked view of the input in a self-distillation setup using a standard Transformer architecture. Instead of predicting modality-specific targets such as words, visual tokens or units of human speech which are local in nature, data2vec predicts contextualized latent representations that contain information from the entire input. Experiments on the major benchmarks of speech recognition, image classification, and natural language understanding demonstrate a new state of the art or competitive performance to predominant approaches.
Spots are limited to keep the discussions organized.
Austin Deep Learning Journal Club is group for committed machine learning practitioners and researchers alike. The group meets every first Tuesdays of each month to discuss research publications. The publications are usually the ones that laid foundation to ML/DL or explore novel promising ideas and are selected by a vote. Participant are expected to read the publications to be able to contribute to discussion and learn from others. This is also a great opportunity to showcase your implementations to get feedback from other experts.
Anyone can suggest and vote for the next paper on Austin Deep Learning slack work space (#paper_group channel): https://austin-deep-learning-slack.herokuapp.com/
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A copy of the paper (either digital or hardcopy)
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