Search Results for author: Alex Nichol

Found 10 papers, 10 papers with code

Shap-E: Generating Conditional 3D Implicit Functions

1 code implementation3 May 2023 Heewoo Jun, Alex Nichol

We present Shap-E, a conditional generative model for 3D assets.

Point-E: A System for Generating 3D Point Clouds from Complex Prompts

1 code implementation16 Dec 2022 Alex Nichol, Heewoo Jun, Prafulla Dhariwal, Pamela Mishkin, Mark Chen

This is in stark contrast to state-of-the-art generative image models, which produce samples in a number of seconds or minutes.

Generating 3D Point Clouds

Hierarchical Text-Conditional Image Generation with CLIP Latents

6 code implementations13 Apr 2022 Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, Mark Chen

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

2 code implementations20 Dec 2021 Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, Mark Chen

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)

Image Inpainting Zero-Shot Text-to-Image Generation

Diffusion Models Beat GANs on Image Synthesis

16 code implementations NeurIPS 2021 Prafulla Dhariwal, Alex Nichol

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.

Conditional Image Generation

Improved Denoising Diffusion Probabilistic Models

9 code implementations18 Feb 2021 Alex Nichol, Prafulla Dhariwal

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)

Denoising Image Generation

VQ-DRAW: A Sequential Discrete VAE

1 code implementation3 Mar 2020 Alex Nichol

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

3 code implementations10 Apr 2018 Alex Nichol, Vicki Pfau, Christopher Hesse, Oleg Klimov, John Schulman

In this report, we present a new reinforcement learning (RL) benchmark based on the Sonic the Hedgehog (TM) video game franchise.

Few-Shot Learning reinforcement-learning +2

On First-Order Meta-Learning Algorithms

12 code implementations8 Mar 2018 Alex Nichol, Joshua Achiam, John Schulman

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.

Few-Shot Image Classification Few-Shot Learning

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