We compare our method with the state-of-the-art on both synthetic and experimental benchmarks. Empirically, cryoSPIN outperforms in reconstruction quality and FSC.
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Check out @clark_kev’s and my paper on fine-tuning diffusion models on differentiable rewards! We present DRaFT, which computes gradients through diffusion sampling. DRaFT is efficient & works across many reward functions.
With @kswersk, @fleet_dj
arXiv: arxiv.org/abs/2309.17400
DRaFT backpropagates the reward directly into LoRA parameters – we don’t need to use RL because diffusion sampling is differentiable. We improve efficiency by truncating the BPTT; even truncating to one step still works! (2/5)
.@clark_kev & I are excited to share our new work on studying Imagen by evaluating it as a zero-shot classifier! Highlights include Imagen achieving SoTA on Stylized Imagenet and being able to perform attribute binding in certain settings unlike CLIP
arxiv.org/abs/2303.15233
🧵👇
1/ We’re thrilled to announce that 3D Flexible Refinement, a motion-based deep generative model for continuous heterogeneity in #cryoEM structures, is available today in #CryoSPARC v4.1 Beta! ❄️⚡
Read more about v4.1: cryosparc.com/updates
We've just released the first version of our Deep Learning Tuning Playbook! This is our attempt to distill our process for actually getting good results with deep learning. We emphasize hyperparameter tuning since it has been a large pain point. github.com/google-research/t…
Happy to announce DreamFusion, our new method for Text-to-3D!
dreamfusion3d.github.io
We optimize a NeRF from scratch using a pretrained text-to-image diffusion model. No 3D data needed!
Joint work w/ the incredible team of @BenMildenhall@ajayj_@jon_barron#dreamfusion
If you don’t think DallE-2 and Imagen are an Alexnet level moment in the machine learning world you aren’t paying attention enough. Very impressive visual results coming out of these. Getting similar chills to when I saw first web browser, iPhone, etc.
"A photo of a giant sloth drinking a cup of coffee in the dawn light"
Generated with #Imagen, a new text-to-image diffusion model from the very smart folks at Google Brain. I'll be sharing much more, so stay tuned!
It was 16 years ago, in 2006, that @geoffreyhinton et al released their demo of deep belief nets. Undergrad me was highly impressed, and helped convince me that deep learning was the way to go. I refreshed Geoff's website almost every day checking for new papers... (1/n)