Instructions & Guidelines
• For this coding assignment, you’re tasked with implementing and training three types of autoencoders: regular autoencoders, variational autoencoders (VAEs), and conditional variational autoencoders (CVAEs). Both the encoder and decoder architectures should follow the ResNet style.
• No extensions will be granted for this assignment under any circumstances.
• Use only data which is provided with this assignment
• Submit only the file named as per the following convention: rollnoA3.py. Ensure strict adherence to this naming convention. Any deviation from it, including the presence of multiple files, .ipynb files, or additional files, will result in non-evaluation. For example, if your roll number is 1234567 or MT34567, your filename should be 1234567A3.py or MT34567A3.py
Coding Guidelines
• Use requirements.txt to setup the environment.
• You will receive a pipeline for training the architectures. Within the folder, locate a file named changerollno.py. Your task is to edit only this specific file .
• In test-set you must score SSIM above 0.6 to be eligible to get evaluated.
1. Train a Denoising AutoEncoder, encoder and decoder must follow ResNet style and residual connection must be after 2 convolution / 2 convolution-batchnorm layer. You are free to pick all other design choices. Plot 3D TSNE embedding plot for logits/embeddings (output from encoder) of whole data after every 10 epochs.
2. Train a Denoising Variational AutoEncoder, encoder and decoder must follow ResNet style and residual connection must be after 2 convolution / 2 convolution-batchnorm layer. You are free to pick all other design choices. Plot 3D TSNE embedding plot for sampled logits/embeddings from logits/embeddings (output from encoder) of whole data after every 10 epochs.
1
