Recipes that I find to be beneficial when working in low-data/imbalance regimes (vision):

* Use a weighted loss function &/or focal loss.
* Either use simpler/shallower models or use models that are known to work well in these cases. Ex: SimCLRV2, Big Transfer, DINO, etc.

1/n
* Use MixUp or CutMix in the augmentation pipeline to relax the space of marginals.
* Ensure a certain percent of minority class data is always present during each mini-batch. In @TensorFlow, this can be done using `rejection_resampling`.

tensorflow.org/guide/data#rej…

2/n
* Use semi-supervised learning recipes that combine the benefits of self-supervision and few-shot learning. Ex: PAWS by @facebookai.
* Use of SWA is generally advised for better generalization but its use in these regimes is particularly useful.

3/n
Has anyone done comprehensive ablations around these? Are there any resources that discuss this area really well?

It probably calls for a paper but I am open to collaborations 😅

4/n

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More from @RisingSayak

26 Apr
New #Keras example is up on *consistency regularization*or an important recipe for semi-supervised learning and tackling distribution shifts as shown in *Noisy Student Training*.

keras.io/examples/visio…

1/n
This example provides a template for performing semi-supervised / weakly supervised learning. A few things one can plug right in:

* Incorporate more data while training the student.
* Filter the high-confidence predictions while training the student.

2/n
The example uses Stochastic Weight Averaging during training the teacher to induce geometric ensembling. With elements like Stochastic Dropout, the performance might even be better.

Here are the full experiments: git.io/JO55v.
Read 5 tweets
2 Dec 20
Got the @TensorFlow Developer Certification.

Thanks to the #ML @GoogleDevExpert program for sponsoring the exam.

In this thread, I will summarize my experience.

⬇️
If you use @TensorFlow in your work moderately, I think you already have the prerequisites. Definitely take the *TensorFlow in Practice* specialization by @lmoroney & @DeepLearningAI_. It will get you up to speed.

Study the contents rigorously.
Review the certificate handbook carefully. It really has all the information you need to know about the certification - tensorflow.org/extras/cert/TF….

* Install @pycharm & get sufficiently comfortable with it.
* Set up the exam environment properly.
Read 6 tweets

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