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Found 10 papers, 10 papers with code
Shap-E: Generating Conditional 3D Implicit Functions
We present Shap-E, a conditional generative model for 3D assets.
Point-E: A System for Generating 3D Point Clouds from Complex Prompts
This is in stark contrast to state-of-the-art generative image models, which produce samples in a number of seconds or minutes.
Hierarchical Text-Conditional Image Generation with CLIP Latents
Contrastive models like CLIP have been shown to learn robust representations of images that capture both semantics and style.
Ranked #27 on
Text-to-Image Generation
on COCO
(using extra training data)
Conditional Image Generation
Zero-Shot Text-to-Image Generation
GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
Diffusion models have recently been shown to generate high-quality synthetic images, especially when paired with a guidance technique to trade off diversity for fidelity.
Ranked #32 on
Text-to-Image Generation
on COCO
(using extra training data)
Evaluating Large Language Models Trained on Code
We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities.
Ranked #1 on
Multi-task Language Understanding
on BBH-alg
Diffusion Models Beat GANs on Image Synthesis
Finally, we find that classifier guidance combines well with upsampling diffusion models, further improving FID to 3. 94 on ImageNet 256$\times$256 and 3. 85 on ImageNet 512$\times$512.
Ranked #1 on
Image Generation
on LSUN Bedroom 256 x 256
(FD metric)
Improved Denoising Diffusion Probabilistic Models
Denoising diffusion probabilistic models (DDPM) are a class of generative models which have recently been shown to produce excellent samples.
Ranked #4 on
Image Generation
on CIFAR-10
(FD metric)
VQ-DRAW: A Sequential Discrete VAE
In this paper, I present VQ-DRAW, an algorithm for learning compact discrete representations of data.
Gotta Learn Fast: A New Benchmark for Generalization in RL
In this report, we present a new reinforcement learning (RL) benchmark based on the Sonic the Hedgehog (TM) video game franchise.
On First-Order Meta-Learning Algorithms
This paper considers meta-learning problems, where there is a distribution of tasks, and we would like to obtain an agent that performs well (i. e., learns quickly) when presented with a previously unseen task sampled from this distribution.
