Search Results for author: Jeff Donahue

Found 26 papers, 17 papers with code

Training Generative Adversarial Networks by Solving Ordinary Differential Equations

1 code implementation NeurIPS 2020 Chongli Qin, Yan Wu, Jost Tobias Springenberg, Andrew Brock, Jeff Donahue, Timothy P. Lillicrap, Pushmeet Kohli

From this perspective, we hypothesise that instabilities in training GANs arise from the integration error in discretising the continuous dynamics.

End-to-End Adversarial Text-to-Speech

2 code implementations ICLR 2021 Jeff Donahue, Sander Dieleman, Mikołaj Bińkowski, Erich Elsen, Karen Simonyan

Modern text-to-speech synthesis pipelines typically involve multiple processing stages, each of which is designed or learnt independently from the rest.

Adversarial Text Dynamic Time Warping +2

High Fidelity Speech Synthesis with Adversarial Networks

3 code implementations ICLR 2020 Mikołaj Bińkowski, Jeff Donahue, Sander Dieleman, Aidan Clark, Erich Elsen, Norman Casagrande, Luis C. Cobo, Karen Simonyan

However, their application in the audio domain has received limited attention, and autoregressive models, such as WaveNet, remain the state of the art in generative modelling of audio signals such as human speech.

Speech Synthesis

Large Scale Adversarial Representation Learning

3 code implementations NeurIPS 2019 Jeff Donahue, Karen Simonyan

We extensively evaluate the representation learning and generation capabilities of these BigBiGAN models, demonstrating that these generation-based models achieve the state of the art in unsupervised representation learning on ImageNet, as well as in unconditional image generation.

Image Generation Self-Supervised Image Classification +3

Large Scale GAN Training for High Fidelity Natural Image Synthesis

26 code implementations ICLR 2019 Andrew Brock, Jeff Donahue, Karen Simonyan

Despite recent progress in generative image modeling, successfully generating high-resolution, diverse samples from complex datasets such as ImageNet remains an elusive goal.

Conditional Image Generation

Population Based Training of Neural Networks

4 code implementations27 Nov 2017 Max Jaderberg, Valentin Dalibard, Simon Osindero, Wojciech M. Czarnecki, Jeff Donahue, Ali Razavi, Oriol Vinyals, Tim Green, Iain Dunning, Karen Simonyan, Chrisantha Fernando, Koray Kavukcuoglu

Neural networks dominate the modern machine learning landscape, but their training and success still suffer from sensitivity to empirical choices of hyperparameters such as model architecture, loss function, and optimisation algorithm.

Machine Translation Model Selection

Visual Discovery at Pinterest

no code implementations15 Feb 2017 Andrew Zhai, Dmitry Kislyuk, Yushi Jing, Michael Feng, Eric Tzeng, Jeff Donahue, Yue Li Du, Trevor Darrell

Over the past three years Pinterest has experimented with several visual search and recommendation services, including Related Pins (2014), Similar Looks (2015), Flashlight (2016) and Lens (2017).

Object Detection

Adversarial Feature Learning

10 code implementations31 May 2016 Jeff Donahue, Philipp Krähenbühl, Trevor Darrell

The ability of the Generative Adversarial Networks (GANs) framework to learn generative models mapping from simple latent distributions to arbitrarily complex data distributions has been demonstrated empirically, with compelling results showing that the latent space of such generators captures semantic variation in the data distribution.

Context Encoders: Feature Learning by Inpainting

10 code implementations CVPR 2016 Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, Alexei A. Efros

In order to succeed at this task, context encoders need to both understand the content of the entire image, as well as produce a plausible hypothesis for the missing part(s).

Generating Visual Explanations

no code implementations28 Mar 2016 Lisa Anne Hendricks, Zeynep Akata, Marcus Rohrbach, Jeff Donahue, Bernt Schiele, Trevor Darrell

Clearly explaining a rationale for a classification decision to an end-user can be as important as the decision itself.

General Classification

Data-dependent Initializations of Convolutional Neural Networks

2 code implementations21 Nov 2015 Philipp Krähenbühl, Carl Doersch, Jeff Donahue, Trevor Darrell

Convolutional Neural Networks spread through computer vision like a wildfire, impacting almost all visual tasks imaginable.

Image Classification Object Detection +1

Visual Search at Pinterest

no code implementations28 May 2015 Yushi Jing, David Liu, Dmitry Kislyuk, Andrew Zhai, Jiajing Xu, Jeff Donahue, Sarah Tavel

We demonstrate that, with the availability of distributed computation platforms such as Amazon Web Services and open-source tools, it is possible for a small engineering team to build, launch and maintain a cost-effective, large-scale visual search system with widely available tools.

Sequence to Sequence -- Video to Text

4 code implementations3 May 2015 Subhashini Venugopalan, Marcus Rohrbach, Jeff Donahue, Raymond Mooney, Trevor Darrell, Kate Saenko

Our LSTM model is trained on video-sentence pairs and learns to associate a sequence of video frames to a sequence of words in order to generate a description of the event in the video clip.

Language Modelling

Long-term Recurrent Convolutional Networks for Visual Recognition and Description

7 code implementations CVPR 2015 Jeff Donahue, Lisa Anne Hendricks, Marcus Rohrbach, Subhashini Venugopalan, Sergio Guadarrama, Kate Saenko, Trevor Darrell

Models based on deep convolutional networks have dominated recent image interpretation tasks; we investigate whether models which are also recurrent, or "temporally deep", are effective for tasks involving sequences, visual and otherwise.

Video Recognition

Part-based R-CNNs for Fine-grained Category Detection

no code implementations15 Jul 2014 Ning Zhang, Jeff Donahue, Ross Girshick, Trevor Darrell

Semantic part localization can facilitate fine-grained categorization by explicitly isolating subtle appearance differences associated with specific object parts.

Fine-Grained Image Classification Object Detection

Caffe: Convolutional Architecture for Fast Feature Embedding

2 code implementations20 Jun 2014 Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, Trevor Darrell

The framework is a BSD-licensed C++ library with Python and MATLAB bindings for training and deploying general-purpose convolutional neural networks and other deep models efficiently on commodity architectures.

Dimensionality Reduction General Classification

One-Shot Adaptation of Supervised Deep Convolutional Models

no code implementations21 Dec 2013 Judy Hoffman, Eric Tzeng, Jeff Donahue, Yangqing Jia, Kate Saenko, Trevor Darrell

In other words, are deep CNNs trained on large amounts of labeled data as susceptible to dataset bias as previous methods have been shown to be?

Domain Adaptation Fine-tuning +1

DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition

8 code implementations6 Oct 2013 Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, Trevor Darrell

We evaluate whether features extracted from the activation of a deep convolutional network trained in a fully supervised fashion on a large, fixed set of object recognition tasks can be re-purposed to novel generic tasks.

Domain Adaptation Object Recognition +2

Efficient Learning of Domain-invariant Image Representations

no code implementations15 Jan 2013 Judy Hoffman, Erik Rodner, Jeff Donahue, Trevor Darrell, Kate Saenko

We present an algorithm that learns representations which explicitly compensate for domain mismatch and which can be efficiently realized as linear classifiers.

Representation Learning

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