Search Results for author: Kuan-Chieh Wang

Found 26 papers, 13 papers with code

Dualing GANs

no code implementations NeurIPS 2017 Yujia Li, Alexander Schwing, Kuan-Chieh Wang, Richard Zemel

We start from linear discriminators in which case conjugate duality provides a mechanism to reformulate the saddle point objective into a maximization problem, such that both the generator and the discriminator of this 'dualing GAN' act in concert.

Neural Relational Inference for Interacting Systems

9 code implementations ICML 2018 Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, Richard Zemel

Interacting systems are prevalent in nature, from dynamical systems in physics to complex societal dynamics.

Adversarial Distillation of Bayesian Neural Network Posteriors

1 code implementation27 Jun 2018 Kuan-Chieh Wang, Paul Vicol, James Lucas, Li Gu, Roger Grosse, Richard Zemel

We propose a framework, Adversarial Posterior Distillation, to distill the SGLD samples using a Generative Adversarial Network (GAN).

Active Learning Anomaly Detection +1

Distilling the Posterior in Bayesian Neural Networks

no code implementations ICML 2018 Kuan-Chieh Wang, Paul Vicol, James Lucas, Li Gu, Roger Grosse, Richard Zemel

We propose a framework, Adversarial Posterior Distillation, to distill the SGLD samples using a Generative Adversarial Network (GAN).

Active Learning Anomaly Detection +1

Centroid-based deep metric learning for speaker recognition

no code implementations6 Feb 2019 Jixuan Wang, Kuan-Chieh Wang, Marc Law, Frank Rudzicz, Michael Brudno

Speaker embedding models that utilize neural networks to map utterances to a space where distances reflect similarity between speakers have driven recent progress in the speaker recognition task.

Few-Shot Image Classification Few-Shot Learning +4

Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling

2 code implementations21 Feb 2019 Jonathan Shen, Patrick Nguyen, Yonghui Wu, Zhifeng Chen, Mia X. Chen, Ye Jia, Anjuli Kannan, Tara Sainath, Yuan Cao, Chung-Cheng Chiu, Yanzhang He, Jan Chorowski, Smit Hinsu, Stella Laurenzo, James Qin, Orhan Firat, Wolfgang Macherey, Suyog Gupta, Ankur Bapna, Shuyuan Zhang, Ruoming Pang, Ron J. Weiss, Rohit Prabhavalkar, Qiao Liang, Benoit Jacob, Bowen Liang, HyoukJoong Lee, Ciprian Chelba, Sébastien Jean, Bo Li, Melvin Johnson, Rohan Anil, Rajat Tibrewal, Xiaobing Liu, Akiko Eriguchi, Navdeep Jaitly, Naveen Ari, Colin Cherry, Parisa Haghani, Otavio Good, Youlong Cheng, Raziel Alvarez, Isaac Caswell, Wei-Ning Hsu, Zongheng Yang, Kuan-Chieh Wang, Ekaterina Gonina, Katrin Tomanek, Ben Vanik, Zelin Wu, Llion Jones, Mike Schuster, Yanping Huang, Dehao Chen, Kazuki Irie, George Foster, John Richardson, Klaus Macherey, Antoine Bruguier, Heiga Zen, Colin Raffel, Shankar Kumar, Kanishka Rao, David Rybach, Matthew Murray, Vijayaditya Peddinti, Maxim Krikun, Michiel A. U. Bacchiani, Thomas B. Jablin, Rob Suderman, Ian Williams, Benjamin Lee, Deepti Bhatia, Justin Carlson, Semih Yavuz, Yu Zhang, Ian McGraw, Max Galkin, Qi Ge, Golan Pundak, Chad Whipkey, Todd Wang, Uri Alon, Dmitry Lepikhin, Ye Tian, Sara Sabour, William Chan, Shubham Toshniwal, Baohua Liao, Michael Nirschl, Pat Rondon

Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models.

Sequence-To-Sequence Speech Recognition

Out-of-distribution Detection in Few-shot Classification

no code implementations25 Sep 2019 Kuan-Chieh Wang, Paul Vicol, Eleni Triantafillou, Chia-Cheng Liu, Richard Zemel

In this work, we propose tasks for out-of-distribution detection in the few-shot setting and establish benchmark datasets, based on four popular few-shot classification datasets.

Classification Out-of-Distribution Detection

On the Invertibility of Invertible Neural Networks

no code implementations25 Sep 2019 Jens Behrmann, Paul Vicol, Kuan-Chieh Wang, Roger B. Grosse, Jörn-Henrik Jacobsen

Guarantees in deep learning are hard to achieve due to the interplay of flexible modeling schemes and complex tasks.

Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling

1 code implementation ICML 2020 Will Grathwohl, Kuan-Chieh Wang, Jorn-Henrik Jacobsen, David Duvenaud, Richard Zemel

We estimate the Stein discrepancy between the data density $p(x)$ and the model density $q(x)$ defined by a vector function of the data.

Understanding and Mitigating Exploding Inverses in Invertible Neural Networks

1 code implementation16 Jun 2020 Jens Behrmann, Paul Vicol, Kuan-Chieh Wang, Roger Grosse, Jörn-Henrik Jacobsen

For problems where global invertibility is necessary, such as applying normalizing flows on OOD data, we show the importance of designing stable INN building blocks.

Probing Few-Shot Generalization with Attributes

no code implementations10 Dec 2020 Mengye Ren, Eleni Triantafillou, Kuan-Chieh Wang, James Lucas, Jake Snell, Xaq Pitkow, Andreas S. Tolias, Richard Zemel

Despite impressive progress in deep learning, generalizing far beyond the training distribution is an important open challenge.

Attribute Few-Shot Learning +1

Exploring representation learning for flexible few-shot tasks

no code implementations1 Jan 2021 Mengye Ren, Eleni Triantafillou, Kuan-Chieh Wang, James Lucas, Jake Snell, Xaq Pitkow, Andreas S. Tolias, Richard Zemel

In this work, we consider a realistic setting where the relationship between examples can change from episode to episode depending on the task context, which is not given to the learner.

Few-Shot Learning Representation Learning

Disentanglement and Generalization Under Correlation Shifts

no code implementations29 Dec 2021 Christina M. Funke, Paul Vicol, Kuan-Chieh Wang, Matthias Kümmerer, Richard Zemel, Matthias Bethge

Exploiting such correlations may increase predictive performance on noisy data; however, often correlations are not robust (e. g., they may change between domains, datasets, or applications) and models that exploit them do not generalize when correlations shift.

Attribute Disentanglement

Variational Model Inversion Attacks

1 code implementation NeurIPS 2021 Kuan-Chieh Wang, Yan Fu, Ke Li, Ashish Khisti, Richard Zemel, Alireza Makhzani

In this work, we provide a probabilistic interpretation of model inversion attacks, and formulate a variational objective that accounts for both diversity and accuracy.

Domain Adaptive 3D Pose Augmentation for In-the-wild Human Mesh Recovery

1 code implementation21 Jun 2022 Zhenzhen Weng, Kuan-Chieh Wang, Angjoo Kanazawa, Serena Yeung

The ability to perceive 3D human bodies from a single image has a multitude of applications ranging from entertainment and robotics to neuroscience and healthcare.

Data Augmentation Domain Adaptation +1

PROB: Probabilistic Objectness for Open World Object Detection

1 code implementation CVPR 2023 Orr Zohar, Kuan-Chieh Wang, Serena Yeung

The resulting Probabilistic Objectness transformer-based open-world detector, PROB, integrates our framework into traditional object detection models, adapting them for the open-world setting.

Object object-detection +1

NeMo: 3D Neural Motion Fields from Multiple Video Instances of the Same Action

1 code implementation28 Dec 2022 Kuan-Chieh Wang, Zhenzhen Weng, Maria Xenochristou, Joao Pedro Araujo, Jeffrey Gu, C. Karen Liu, Serena Yeung

Empirically, we show that NeMo can recover 3D motion in sports using videos from the Penn Action dataset, where NeMo outperforms existing HMR methods in terms of 2D keypoint detection.

3D Reconstruction Human Mesh Recovery +1

NeMo: Learning 3D Neural Motion Fields From Multiple Video Instances of the Same Action

no code implementations CVPR 2023 Kuan-Chieh Wang, Zhenzhen Weng, Maria Xenochristou, João Pedro Araújo, Jeffrey Gu, Karen Liu, Serena Yeung

Empirically, we show that NeMo can recover 3D motion in sports using videos from the Penn Action dataset, where NeMo outperforms existing HMR methods in terms of 2D keypoint detection.

3D Reconstruction Human Mesh Recovery +1

Diagnosing and Rectifying Vision Models using Language

1 code implementation8 Feb 2023 Yuhui Zhang, Jeff Z. HaoChen, Shih-Cheng Huang, Kuan-Chieh Wang, James Zou, Serena Yeung

Our proposed method can discover high-error data slices, identify influential attributes and further rectify undesirable model behaviors, without requiring any visual data.

Contrastive Learning

LOVM: Language-Only Vision Model Selection

1 code implementation NeurIPS 2023 Orr Zohar, Shih-Cheng Huang, Kuan-Chieh Wang, Serena Yeung

As the number of open-source VLM variants increases, there is a need for an efficient model selection strategy that does not require access to a curated evaluation dataset.

Model Selection

Generalizable Neural Fields as Partially Observed Neural Processes

no code implementations ICCV 2023 Jeffrey Gu, Kuan-Chieh Wang, Serena Yeung

Neural fields, which represent signals as a function parameterized by a neural network, are a promising alternative to traditional discrete vector or grid-based representations.

Meta-Learning

Viewpoint Textual Inversion: Unleashing Novel View Synthesis with Pretrained 2D Diffusion Models

no code implementations14 Sep 2023 James Burgess, Kuan-Chieh Wang, Serena Yeung

Our method, Viewpoint Neural Textual Inversion (ViewNeTI), controls the 3D viewpoint of objects in generated images from frozen diffusion models.

Novel View Synthesis

Open World Object Detection in the Era of Foundation Models

no code implementations10 Dec 2023 Orr Zohar, Alejandro Lozano, Shelly Goel, Serena Yeung, Kuan-Chieh Wang

We exploit the inherent connection between classes in application-driven datasets and introduce a novel method, Foundation Object detection Model for the Open world, or FOMO, which identifies unknown objects based on their shared attributes with the base known objects.

Object object-detection +1

Iterative Motion Editing with Natural Language

no code implementations15 Dec 2023 Purvi Goel, Kuan-Chieh Wang, C. Karen Liu, Kayvon Fatahalian

Text-to-motion diffusion models can generate realistic animations from text prompts, but do not support fine-grained motion editing controls.

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