deeplense-2025-results

✨ DeepLense Submission Proposal - 2025 ✨

• Full Code is Available at : https://github.com/Sar2580P/DeepLenseSubmissionProposal-2025
• This hugging face repo contains the model checkpoints and loggings.

🔭 Project of Interest:

• My primary focus lies in Diffusion Models. I've been deeply engaged with the advancements in this exciting area of generative AI.
• I possess a strong theoretical and mathematical understanding, and I have implemented key diffusion model architectures such as DDPM, DDIM, and Latent Diffusion Models.
• As an optional third task, I explored Foundation Models. This was a new and enriching experience, providing valuable insights into large-scale pre-training and the adaptation of learned representations for diverse downstream tasks like classification and super-resolution.

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📊 1. Common Multi-class Classification:

• I fine-tuned a Resnet-18 model (pretrained on ImageNet) to classify gravitational lensing images into the following categories:     - No Substructure     - ️Subhalo Substructure     - Vortex Substructure
• Model performance was rigorously evaluated using key metrics: Accuracy, Kappa, ROC-AUC curve, and the MSE loss curve.

🌌 2. Specific Test IV. Diffusion Models:

• I successfully trained a DDIM model to generate realistic strong gravitational lensing images.
• To accommodate larger batch sizes, the model training was distributed across 2 GPUs.
• The foundational architecture code for the DDIM/DDPM implementation draws inspiration from this excellent open-source repository.

🧠 3. Specific Test VI. Foundation Model:

• I undertook the pre-training of a ViT as an encoder, coupled with a lightweight decoder, utilizing the Masked AutoEncoder (MAE) technique.
• Task VI-A (Classification): I employed the pre-trained ViT with an MLP head as the encoder for classification tasks.
• Task VI-B (Super-Resolution): I integrated the pre-trained ViT with a more powerful decoder to perform super-resolution.
• This insightful blog post proved invaluable for understanding and implementing the Masked AutoEncoder.

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✨ It was a truly engaging project, and I'm delighted to have had such an exciting and insightful learning experience. Thank you for the opportunity! ✨