This work is performed as term-project for CSCI-GA 3033-091: Practical Deep Learning Systems at NYU Courant. In this project, we study and evaluate the impact of attention-based blocks on learned internal representations of vision models; varying from attention-free models (CNNs) to pure self-attention based models (ViT). We use state-of-the-art metrics: Centered Kernel Alignment (CKA) following the work of Raghu et al. (NeurIPS 2021) based on Hilbert-Schmidt Independence Criterion (HSIC) to understand the similarity across learned representations in various hidden layers. We performed our experiments on three state-of-the-art vision models namely: CNNs (ResNet-18,34,50), Hybrid networks (Lambda ResNet-26,38,50) and Vision Transformer (ViT-large_patch32_224). We evaluated the CNNs and hybrid networks on CIFAR-100 dataset and ViT on Imagenet dataset.

Visualizing CNNs

Visualizing Lambda networks

Visualizing ViTs

We recommend using Anaconda environment to install pre-requisites packages for running our framework and models. We recommend installing the packages using requirements.txt file provided in our repository.

We list down the packages which we used on our side for experimentations.

• cudatoolkit = 11.3
• numpy >= 1.20.1
• pandas >= 1.2.2
• pickleshare >= 0.7.5
• python =3.9
• pytorch = 1.11.0
• scikit-learn = 0.24.1
• tqdm >= 4.56
• seaborn >= 0.11.1
• torchvision = 0.12
• timm

├── images
│   ├── lambda.jpg
│   ├── resnet.jpg
│   └── vit.jpg
├── LICENSE
├── notebooks
│   ├── Lambda_R26_CIFAR100_PRETRAINED.ipynb
│   ├── Lambda_R26_CIFAR100_RANDOM.ipynb
│   ├── Lambda_R38_CIFAR100_PRETRAINED.ipynb
│   ├── Lambda_R50_CIFAR100_PRETRAINED.ipynb
│   ├── Lambda_R50_CIFAR100_RANDOM.ipynb
│   ├── R101_CIFAR100.ipynb
│   ├── R18_CIFAR100_PRETRAINED.ipynb
│   ├── R18_CIFAR100_RANDOM.ipynb
│   ├── R34_CIFAR100_PRETRAINED.ipynb
│   ├── R34_CIFAR100_RANDOM.ipynb
│   ├── R50_CIFAR100_PRETRAINED.ipynb
│   ├── R50_CIFAR100_RANDOM.ipynb
│   ├── ViT_ImageNet_PRETRAINED.ipynb
│   └── ViT_ImageNet_RANDOM.ipynb
├── README.md
└── utilities
    ├── lambda_layer.py         # The file contains the definition of lambda layers for lambda resnets
    ├── lambda_resnet.py	# Utility file defining lambda network architecture
    ├── metrics.py		# Utility script computing Gaussian-CKA and RBF-CKA
    ├── resnet.py		# Utility script containing ResNet model architecture
    ├── train.py                # Training script for CIFAR-100
    └── utils.py                # Helper functions utility script for hooking,training and validation and CKA score computation

1. In notebooks directory, we have kept all the notebooks which were used to generate the visualization. The files having random as suffix denotes that the visualization for the model was generated with randomly initialized weights. Notebooks having pretrained suffix have visualizations with models trained on relevant dataset. One just needs to run the notebooks and get the results.

2. In utilities directory, we have kept various python utilities having model architecture design, metrics, training scripts and helper functions required for training and validation.

Run the following command for training the models

python train.py <resnet_model_name> <num_epochs_to_train> <seed> <model_dump_location>

jupyter nbconvert --execute <notebookname.ipynb>