Key Learnings & Research Insights
This page documents key concepts, strategies, and research papers I studied to deepen my understanding during the internship.
AI/ML Concepts
-
Unsupervised Pretraining
Explored the effectiveness of unsupervised feature learning using autoencoders and its impact on downstream classification. -
Fine-Tuning
Studied how frozen vs. trainable pretrained layers influence performance when shifting to a supervised task (e.g., even/odd digit classification). -
ControlNet for Inpainting
Learned how ControlNet conditions generative models with segmentation/masks for controlled outputs in Stable Diffusion pipelines.
Research Papers Studied
| Title | Source | Summary |
|---|---|---|
| Multimodal Pretraining for Vision-and-Language Tasks | arXiv | Describes how joint learning on text and images can improve performance on downstream visual-language tasks. |
| Taming Transformers for High-Resolution Image Synthesis | arXiv | Explains how VQ-GAN enables high-resolution generation using transformers with discrete latent spaces. |
| Label Studio: Data labeling platform | Label Studio Docs | Explored its architecture, use cases, and persistent storage setup via Docker. |
| ControlNet: Adding Conditional Control to Diffusion Models | arXiv | Introduces the mechanism of adding structure-aware control to Stable Diffusion through a conditional branch. |
Personal Reflections
These papers and tools helped me:
- Understand how vision and language models interact
- Realize the value of unsupervised representation learning
- Think modularly when designing inpainting workflows
- Appreciate the infrastructure side (Docker, storage, etc.) of ML tools
Links to Further Study
“Learning isn’t just about applying — it’s about understanding the why behind the what.”