DINOSim was created to provide a functional tool for environments with limited resources, little data, no labels, or no access to large model training, a common challenge in many biomedical labs.
A few months ago, I had the opportunity to present my project, DINOSim, at #SPAOM2024. It was an incredible experience where I had the opportunity to meet and share ideas with many amazing people.
🧪🔬 Are you at #SPAOM2024 ? Here you have a list of the contributions from my lab (all in Day 2 and 3)👇
1⃣ Tomorrow at 10:15am (Sala Toledo) my PhD student @AAitorG will present his work "Zero-Shot Object Detection with Foundational Models: A Similarity-Based Approach"
🆕 We have updated our preprint in @biorxivpreprint 📰 It explains better the current state of #BiaPy ⛴️ while describing as well its limitations 🤗 Hope you like it!
"BiaPy: Accessible deep learning on bioimages" https://t.co/nAJ5159xVs
I remember during my PhD,
I spent many many hours reviewing every single image in my datasets, while I just used an off-the-shelf GAN architecture for my models because I observed that
dataset quality >>>> model arch
DINOv2, the cutting-edge computer vision model trained through self-supervised learning to produce universal features, is now available under the Apache 2.0 license.
Onward with open source AI.
I-JEPA: Efficient method for Self-Supervised Learning of image features.
No need for data augmentation, just masking.
Joint embedding predictive architecture, not generative.
And it's open source, of course.
Blog: https://t.co/ZuouZgeEMC
Paper: https://t.co/BoHSnELyw8
Code & models: https://t.co/DgS9XiwnMz
GPT-4 "discovered" the same sorting algorithm as AlphaDev by removing "mov S P".
No RL needed. Can I publish this on nature?
here are the prompts I used https://t.co/FWAsE81lyq
(excuse my idiotic typos, but gpt4 doesn't mind anyways)
#OpenAI is planning to stop #ChatGPT users from making social media bots and cheating on homework by "watermarking" outputs. How well could this really work? Here's just 23 words from a 1.3B parameter watermarked LLM. We detected it with 99.999999999994% confidence. Here's how 🧵
This is a "3D-diffusion" video created using a combination of four different AI models🤯
Welcome to the metaverse! 🌌😎
There's such incredible potential here that I want to explain how I made this, so here's a thread! (1/n)