From Model Airplanes to Model Architectures: A Personal Performance Review

As this semester comes to a close, so does my blog series on Latent Diffusion Models. We've journeyed from my initial curiosity and a deep dive into the rhetoric of a landmark paper, to an analysis of its scientific impact and meteoric rise as a cultural phenomenon. For this final post, I'm turning the critical lens I used on the Rombach et al. paper inward. It's time to assess my own performance, reflect on my learning process, and consider where I go from here.

A Self-Assessment of My Scientific Skills

This module was designed to build a specific set of skills. Here is my honest assessment of where I stand with each of them:

• Can I read and understand a scientific paper? Yes, and far more deeply than before. I began the semester reading papers for their conclusions; I now read them for their arguments. My analysis of the LDM paper's structure and its subtle rhetorical choices taught me to look beyond the methods and results and to question the narrative the authors are crafting. I can now form my own scientific opinion, confidently appreciating the paper's genius while also criticizing its significant gaps in reproducibility.

• Can I obtain information and put research in context? I’ve made significant strides here. I can now confidently place Latent Diffusion Models within the broader history of generative models, understanding how they build upon autoencoders and older diffusion principles to solve the efficiency bottleneck. My literature research is effective but remains somewhat intuitive. It’s an area where I see clear potential for improvement.

• Can I present scientific results in different ways? I believe this is a strength. This blog has been an exercise in narrative-driven communication, aiming to tell a compelling story about a piece of technology. I’m comfortable adapting my style, knowing that a concise oral presentation requires powerful visuals, while a formal academic document demands the precision of LaTeX and rigorous citation.

• Can I engage in scientific discussion and formulate criticism? This is a mixed bag. I feel very comfortable formulating written criticism, as demonstrated in my posts. However, I am still developing confidence in live, spontaneous scientific debate. Like many, I often need time to reflect before I can articulate a question or a critique effectively. Regarding accepting criticism, the feedback on this blog has been invaluable, helping me to see my work from different perspectives and strengthen my arguments.

How Do I Assess My Performance, Compared to My Peers?

Comparing my work to that of past participants has been an incredibly insightful exercise. It highlights that there are many valid and effective ways to approach scientific communication.

• Comparison with Javorka Acimovic: Javorka’s self-assessment is a model of structured, methodical reflection. Her use of checklists, tables, and specific tools like Research Rabbit reveals a highly organized and analytical approach. In contrast, my style has been more narrative-driven. My goal was to weave a continuous story about the LDM paper across my posts.

  • What I did better: I believe my strength lies in this narrative cohesion, taking the reader on a complete journey from a paper's inception to its societal impact.

  • What I could learn: I could greatly benefit from her systematic approach to literature retrieval. My "intuitive" method works, but it's less efficient and repeatable than her structured process.

• Comparison with Jakub Hanuska: Jakub’s review of the Vision Transformer (ViT) paper was a masterclass in clarity. His "How ViT works – step by step" section was brilliant for distilling a complex architecture into an easily digestible format. While I explained the what and why of LDMs, Jakub excelled at explaining the how. I can learn from his ability to structure purely technical explanations for maximum clarity.

• Comparison with Murali Ganesh Shiva: Ganesh’s post on camera calibration demonstrated a fantastic ability to perform a deep dive into a specific application domain, synthesizing information from multiple sources to explain how AI is solving a tangible problem. My focus on a single, foundational paper was different. Ganesh inspires me to always connect research back to its practical, real-world utility.

Where I Still Have Need for Improvement

This comparison makes my areas for improvement clear. While my narrative style is effective, I need to incorporate more of Javorka’s systematic rigor into my research process. I need to practice Jakub’s clarity in technical breakdowns and strive to ground my analysis in practical applications, as Ganesh did so well. Most importantly, I need to push myself out of my comfort zone and engage more actively in live scientific discussions.

My Next Steps

My biggest takeaway from this seminar is that becoming a scientist is not just about understanding models; it's about joining a global conversation. To that end, I have a few concrete goals for my upcoming Master's thesis:

1. Systematize My Research: I will actively use tools like Research Rabbit or Scite.ai to make my literature search more robust and efficient.

2. Practice Technical Distillation: For every complex concept I encounter, I will practice writing a clear, step-by-step explanation, as if I were writing a blog post for my peers.

3. Embrace the Debate: I will make a point of formulating at least one question or comment during every research colloquium I attend, even if it feels uncomfortable at first.

This module has been a challenging but incredibly rewarding journey. It has transformed me from a passive consumer of science into an active, critical, and I hope, more thoughtful participant.