,1,#stablediffusion #ai #stabilityai
An interview with Emad Mostaque, founder of Stability AI.
OUTLINE:
0:00 - Intro
1:30 - What is Stability AI?
3:45 - Where does the money come from?
5:20 - Is this the CERN of AI?
6:15 - Who gets access to the resources?
8:00 - What is Stable Diffusion?
11:40 - What if your model produces bad outputs?
14:20 - Do you employ people?
16:35 - Can you prevent the corruption of profit?
19:50 - How can people find you?
22:45 - Final thoughts, let's destroy PowerPoint
Links:
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,1,#nerf #neuralrendering #deeplearning
View Synthesis is a tricky problem, especially when only given a sparse set of images as an input. NeRF embeds an entire scene into the weights of a feedforward neural network, trained by backpropagation through a differential volume rendering procedure, and achieves state-of-the-art view synthesis. It includes directional dependence and is able to capture fine structural details, as well as reflection effects and transparency.
OUTLINE:
0:00 - Intro & Overview
4:50 - View Synthesis Task Description
5:50 - The fundamental difference to classic Deep Learning
7:00 - NeRF Core Concept
15:30 - Training the NeRF from sparse views
20:50 - Radiance Field Volume Rendering
23:20 - Resulting View Dependence
24:00 - Positional Encoding
28:00 - Hierarchical Volume Sampling
30:15 - Experimental Results
33:30 - Comments & Conclusion
Paper: https://arxiv.org/abs/2003.08934
Website & Code: https://www.matthewtancik.com/nerf
My Video on SIREN: https://youtu.be/Q5g3p9Zwjrk
Abstract:
We present a method that achieves state-of-the-art results for synthesizing novel views of complex scenes by optimizing an underlying continuous volumetric scene function using a sparse set of input views. Our algorithm represents a scene using a fully-connected (non-convolutional) deep network, whose input is a single continuous 5D coordinate (spatial location (x,y,z) and viewing direction (θ,ϕ)) and whose output is the volume density and view-dependent emitted radiance at that spatial location. We synthesize views by querying 5D coordinates along camera rays and use classic volume rendering techniques to project the output colors and densities into an image. Because volume rendering is naturally differentiable, the only input required to optimize our representation is a set of images with known camera poses. We describe how to effectively optimize neural radiance fields to render photorealistic novel views of scenes with complicated geometry and appearance, and demonstrate results that outperform prior work on neural rendering and view synthesis. View synthesis results are best viewed as videos, so we urge readers to view our supplementary video for convincing comparisons.
Authors: Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, Ren Ng
Links:
TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick
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BiliBili: https://space.bilibili.com/1824646584
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,1,A look at OpenAI's new GPT-2 model and the surrounding controversy.
https://blog.openai.com/better-language-models/
Abstract:
Natural language processing tasks, such as question answering, machine translation, reading comprehension, and summarization, are typically approached with supervised learning on taskspecific datasets. We demonstrate that language models begin to learn these tasks without any explicit supervision when trained on a new dataset of millions of webpages called WebText. When conditioned on a document plus questions, the answers generated by the language model reach 55 F1 on the CoQA dataset - matching or exceeding the performance of 3 out of 4 baseline systems without using the 127,000+ training examples. The capacity of the language model is essential to the success of zero-shot task transfer and increasing it improves performance in a log-linear fashion across tasks. Our largest model, GPT-2, is a 1.5B parameter Transformer that achieves state of the art results on 7 out of 8 tested language modeling datasets in a zero-shot setting but still underfits WebText. Samples from the model reflect these improvements and contain coherent paragraphs of text. These findings suggest a promising path towards building language processing systems which learn to perform tasks from their naturally occurring demonstrations.
Authors:
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever
