SMAI Project

Understanding Deep Learning Requires Rethinking Generalization (arxiv)

Aim:

To understand what differentiates neural networks that generalize well from those that do not

Datasets:

CIFAR10, ImageNet

Models:

MLP-512, Inception (tiny), Wide ResNet, AlexNet, Inception_v3

Experiments done:

• Effect of explicit regularization like augmentation, weight decay, dropout
• Effect of implicit regularization like BatchNorm
• Input data corruption: Pixel shuffle, Gaussian pixels, Random pixels
• Label corruption with different corruption levels from 1 to 100 %

Results

Data Corruption experiments

Label corruption experiments

Regularization experiments

Checkpoint files of Model Trained on ImageNet (Explicit Regularization):

• w/o Augmentation, Learning Rate Scheduler, Dropout: checkpoint
  
• w/o Augmentation, w/o Learning Rate Scheduler, Dropout : checkpoint
  
• with Augmentation, Learning Rate Scheduler, Dropout : checkpoint

Team Members:

• Siddhant Bansal (@Sid2697)
• Piyush Singh (@piyush-kgp)
• Madhav Agarwal (@mdv3101)