Semi Supervised

1. Paper review

2. Ruder an overview of proxy labeled for semi supervised (AMAZING)

3. Self training

   1. Self training and tri training

   2. Confidence regularized self training

   3. Domain adaptation for semantic segmentation using class balanced self-training

   4. Self labeled techniques for semi supervised learning

4. Tri training

   1. Trinet for semi supervised Deep learning

   2. Tri training exploiting unlabeled data using 3 classes, paper

   3. Improving tri training with unlabeled data

   4. Tri training using NN ensemble

   5. Asymmetric try training for unsupervised domain adaptation, another implementation, another, paper

   6. Tri training git

5. Fast ai forums

6. UDA GIT, paper, medium*, medium 2 (has data augmentation articles)

7. s4l

8. Google’s UDM and MixMatch dissected- For text classification, the authors used a combination of back translation and a new method called TF-IDF based word replacing.

Back translation consists of translating a sentence into some other intermediate language (e.g. French) and then translating it back to the original language (English in this case). The authors trained an English-to-French and French-to-English system on the WMT 14 corpus.

TF-IDF word replacement replaces words in a sentence at random based on the TF-IDF scores of each word (words with a lower TF-IDF have a higher probability of being replaced).

1. MixMatch, medium, 2, 3, 4, that works by guessing low-entropy labels for data-augmented unlabeled examples and mixing labeled and unlabeled data using MixUp. We show that MixMatch obtains state-of-the-art results by a large margin across many datasets and labeled data amounts

2. ReMixMatch - paper is really good. “We improve the recently-proposed “MixMatch” semi-supervised learning algorithm by introducing two new techniques: distribution alignment and augmentation anchoring”

3. FixMatch - FixMatch is a recent semi-supervised approach by Sohn et al. from Google Brain that improved the state of the art in semi-supervised learning(SSL). It is a simpler combination of previous methods such as UDA and ReMixMatch.

   Image via Amit Chaudhary wrong credit? let me know

4. Curriculum Labeling: Self-paced Pseudo-Labeling for Semi-Supervised Learning

5. FAIR 2 original, Summarization of FAIR’s student teacher weak/ semi supervision

6. Leveraging Just a Few Keywords for Fine-Grained Aspect Detection Through Weakly Supervised Co-Training

7. Fidelity-Weighted Learning - “fidelity-weighted learning” (FWL), a semi-supervised student- teacher approach for training deep neural networks using weakly-labeled data. FWL modulates the parameter updates to a student network (trained on the task we care about) on a per-sample basis according to the posterior confidence of its label-quality estimated by a teacher (who has access to the high-quality labels). Both student and teacher are learned from the data."

8. Unproven student teacher git

9. A really nice student teacher git with examples.

Image by yuanli2333. wrong credit? let me know

10. Teacher student for tri training for unlabeled data exploitation

Image by the late Dr. Hui Li, @ SAS. wrong credit? let me know