Search Results for author: Richard Zhang

Found 28 papers, 21 papers with code

Aligning and Projecting Images to Class-conditional Generative Networks

no code implementations ECCV 2020 Minyoung Huh, Richard Zhang, Jun-Yan Zhu, Sylvain Paris, Aaron Hertzmann

We present a method for projecting an input image into the space of a class-conditional generative neural network.

Editing Conditional Radiance Fields

1 code implementation13 May 2021 Steven Liu, Xiuming Zhang, Zhoutong Zhang, Richard Zhang, Jun-Yan Zhu, Bryan Russell

In this paper, we explore enabling user editing of a category-level NeRF - also known as a conditional radiance field - trained on a shape category.

Novel View Synthesis

Ensembling with Deep Generative Views

no code implementations CVPR 2021 Lucy Chai, Jun-Yan Zhu, Eli Shechtman, Phillip Isola, Richard Zhang

Here, we investigate whether such views can be applied to real images to benefit downstream analysis tasks such as image classification.

Image Classification

On Buggy Resizing Libraries and Surprising Subtleties in FID Calculation

2 code implementations22 Apr 2021 Gaurav Parmar, Richard Zhang, Jun-Yan Zhu

We investigate the sensitivity of the Fr\'echet Inception Distance (FID) score to inconsistent and often incorrect implementations across different image processing libraries.

Few-shot Image Generation via Cross-domain Correspondence

no code implementations CVPR 2021 Utkarsh Ojha, Yijun Li, Jingwan Lu, Alexei A. Efros, Yong Jae Lee, Eli Shechtman, Richard Zhang

Training generative models, such as GANs, on a target domain containing limited examples (e. g., 10) can easily result in overfitting.

Image Generation

The Low-Rank Simplicity Bias in Deep Networks

1 code implementation18 Mar 2021 Minyoung Huh, Hossein Mobahi, Richard Zhang, Brian Cheung, Pulkit Agrawal, Phillip Isola

We investigate the hypothesis that deeper nets are implicitly biased to find lower rank solutions and that these are the solutions that generalize well.

Image Classification

CDPAM: Contrastive learning for perceptual audio similarity

1 code implementation9 Feb 2021 Pranay Manocha, Zeyu Jin, Richard Zhang, Adam Finkelstein

The DPAM approach of Manocha et al. learns a full-reference metric trained directly on human judgments, and thus correlates well with human perception.

Contrastive Learning Speech Synthesis

Few-shot Image Generation with Elastic Weight Consolidation

no code implementations NeurIPS 2020 Yijun Li, Richard Zhang, Jingwan Lu, Eli Shechtman

Few-shot image generation seeks to generate more data of a given domain, with only few available training examples.

Image Generation

How many samples is a good initial point worth in Low-rank Matrix Recovery?

no code implementations NeurIPS 2020 Jialun Zhang, Richard Zhang

Optimizing the threshold over regions of the landscape, we see that, for initial points not too close to the ground truth, a linear improvement in the quality of the initial guess amounts to a constant factor improvement in the sample complexity.

Contrastive Learning for Unpaired Image-to-Image Translation

6 code implementations30 Jul 2020 Taesung Park, Alexei A. Efros, Richard Zhang, Jun-Yan Zhu

Furthermore, we draw negatives from within the input image itself, rather than from the rest of the dataset.

Contrastive Learning Image-to-Image Translation

Swapping Autoencoder for Deep Image Manipulation

3 code implementations NeurIPS 2020 Taesung Park, Jun-Yan Zhu, Oliver Wang, Jingwan Lu, Eli Shechtman, Alexei A. Efros, Richard Zhang

Deep generative models have become increasingly effective at producing realistic images from randomly sampled seeds, but using such models for controllable manipulation of existing images remains challenging.

Image Manipulation

Transforming and Projecting Images into Class-conditional Generative Networks

2 code implementations4 May 2020 Minyoung Huh, Richard Zhang, Jun-Yan Zhu, Sylvain Paris, Aaron Hertzmann

We present a method for projecting an input image into the space of a class-conditional generative neural network.

Image Morphing with Perceptual Constraints and STN Alignment

1 code implementation29 Apr 2020 Noa Fish, Richard Zhang, Lilach Perry, Daniel Cohen-Or, Eli Shechtman, Connelly Barnes

In image morphing, a sequence of plausible frames are synthesized and composited together to form a smooth transformation between given instances.

Image Morphing

A Differentiable Perceptual Audio Metric Learned from Just Noticeable Differences

1 code implementation13 Jan 2020 Pranay Manocha, Adam Finkelstein, Zeyu Jin, Nicholas J. Bryan, Richard Zhang, Gautham J. Mysore

Assessment of many audio processing tasks relies on subjective evaluation which is time-consuming and expensive.

Denoising

CNN-generated images are surprisingly easy to spot... for now

2 code implementations CVPR 2020 Sheng-Yu Wang, Oliver Wang, Richard Zhang, Andrew Owens, Alexei A. Efros

In this work we ask whether it is possible to create a "universal" detector for telling apart real images from these generated by a CNN, regardless of architecture or dataset used.

Data Augmentation Image Generation +1

Detecting Photoshopped Faces by Scripting Photoshop

1 code implementation ICCV 2019 Sheng-Yu Wang, Oliver Wang, Andrew Owens, Richard Zhang, Alexei A. Efros

Most malicious photo manipulations are created using standard image editing tools, such as Adobe Photoshop.

Image Manipulation Detection

Making Convolutional Networks Shift-Invariant Again

7 code implementations25 Apr 2019 Richard Zhang

The well-known signal processing fix is anti-aliasing by low-pass filtering before downsampling.

Classification Consistency Conditional Image Generation

Deep Parametric Shape Predictions using Distance Fields

1 code implementation CVPR 2020 Dmitriy Smirnov, Matthew Fisher, Vladimir G. Kim, Richard Zhang, Justin Solomon

Many tasks in graphics and vision demand machinery for converting shapes into consistent representations with sparse sets of parameters; these representations facilitate rendering, editing, and storage.

Stochastic Adversarial Video Prediction

3 code implementations ICLR 2019 Alex X. Lee, Richard Zhang, Frederik Ebert, Pieter Abbeel, Chelsea Finn, Sergey Levine

However, learning to predict raw future observations, such as frames in a video, is exceedingly challenging -- the ambiguous nature of the problem can cause a naively designed model to average together possible futures into a single, blurry prediction.

Representation Learning Video Generation +1

The Unreasonable Effectiveness of Deep Features as a Perceptual Metric

24 code implementations CVPR 2018 Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, Oliver Wang

We systematically evaluate deep features across different architectures and tasks and compare them with classic metrics.

SSIM

Self-Supervised Learning of Object Motion Through Adversarial Video Prediction

no code implementations ICLR 2018 Alex X. Lee, Frederik Ebert, Richard Zhang, Chelsea Finn, Pieter Abbeel, Sergey Levine

In this paper, we study the problem of multi-step video prediction, where the goal is to predict a sequence of future frames conditioned on a short context.

Self-Supervised Learning Video Prediction

Real-Time User-Guided Image Colorization with Learned Deep Priors

3 code implementations8 May 2017 Richard Zhang, Jun-Yan Zhu, Phillip Isola, Xinyang Geng, Angela S. Lin, Tianhe Yu, Alexei A. Efros

The system directly maps a grayscale image, along with sparse, local user "hints" to an output colorization with a Convolutional Neural Network (CNN).

Colorization

Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction

2 code implementations CVPR 2017 Richard Zhang, Phillip Isola, Alexei A. Efros

We propose split-brain autoencoders, a straightforward modification of the traditional autoencoder architecture, for unsupervised representation learning.

Transfer Learning Unsupervised Representation Learning

Colorful Image Colorization

35 code implementations28 Mar 2016 Richard Zhang, Phillip Isola, Alexei A. Efros

We embrace the underlying uncertainty of the problem by posing it as a classification task and use class-rebalancing at training time to increase the diversity of colors in the result.

Colorization Self-Supervised Image Classification

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