uda
SSL4MIS
uda | SSL4MIS | |
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2 | 2 | |
2,153 | 1,996 | |
0.0% | 1.8% | |
0.0 | 6.2 | |
over 2 years ago | 9 months ago | |
Python | Python | |
Apache License 2.0 | MIT License |
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uda
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BERT models: how resilient are they to typos?
Another thought is to do some data augmentation using back-translation, a la https://arxiv.org/abs/1904.12848
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A Visual Survey of Data Augmentation in NLP
The words that replaces the original word are chosen by calculating TF-IDF scores of words over the whole document and taking the lowest ones. You can refer to the code implementation for this in the original paper here.
SSL4MIS
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Researchers at Oxford University Propose a Machine Learning Framework Called ‘TriSegNet’ Based on Triple-View Feature Learning for Medical Image Segmentation
Continue reading | Check out the paper and github link
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How to get image dataset annotated? Any idea?
Otherwise, you may be able to look into semi-supervised learning. Basically, you label a subset of your data, and use semi-supervised techniques to extrapolate and label the rest. This, of course, is a challenge in itself, but luckily this particular challenge has been researched a lot, so you may find something to get started with.
What are some alternatives?
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
cleanlab - The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
nlpaug - Data augmentation for NLP
awesome-data-labeling - A curated list of awesome data labeling tools
clip-as-service - 🏄 Scalable embedding, reasoning, ranking for images and sentences with CLIP
alibi-detect - Algorithms for outlier, adversarial and drift detection
contractions - Fixes contractions such as `you're` to `you are`
cleanlab - The standard package for machine learning with noisy labels and finding mislabeled data. Works with most datasets and models. [Moved to: https://github.com/cleanlab/cleanlab]
squirrel-datasets-core - Squirrel dataset hub
bert - TensorFlow code and pre-trained models for BERT
squirrel-core - A Python library that enables ML teams to share, load, and transform data in a collaborative, flexible, and efficient way :chestnut: