nikheelpandey / TAUP-PyTorch

Task Agnostic Unsupervised Learning

Task Agnosgtic Unsupervised Pretraining

Crude implementation of simCLR paper. Used the LARS optimizer over the contrastive loss function as described in the paper to train a contrastive model. Used this model as encoder and added fully connected layer to create a classifier.

Below is the observations for the classifer trained using TAUP and supervised learning.

model	num_samples	accuracy	epochs
Supervised Learning	50000	97.3	103
TAUP+Supervised Finetuning	5000	93.5	45
TAUP+Supervised Finetuning	10000	93.9	23
TAUP+Supervised Finetuning	10000	95.34	45

Scripts

• To train the TAUP model with contrastive loss: train_taup.py
• To train the clf over the TAUP model : train_classifier.py

To Do:

• Test the effect of BatchNormalization in the projection head
• Add the knowledge distilation part
• Test it out on a dataset more complicated than CIFAR

Reference: https://arxiv.org/pdf/2006.10029.pdf