Deepak Pathak

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Carnegie Mellon University ∙ berkeley college

Deepak Pathak is a PhD student focused on computer vision and machine learning.

One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Control

Reinforcement learning is typically concerned with learning control poli...
07/09/2020 ∙ by Wenlong Huang, et al. ∙ 9 ∙ share

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Locally Masked Convolution for Autoregressive Models

High-dimensional generative models have many applications including imag...
06/22/2020 ∙ by Ajay Jain, et al. ∙ 60 ∙ share

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Planning to Explore via Self-Supervised World Models

Reinforcement learning allows solving complex tasks, however, the learni...
05/12/2020 ∙ by Ramanan Sekar, et al. ∙ 2 ∙ share

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Sparse Graphical Memory for Robust Planning

To operate effectively in the real world, artificial agents must act fro...
03/13/2020 ∙ by Michael Laskin, et al. ∙ 15 ∙ share

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Third-Person Visual Imitation Learning via Decoupled Hierarchical Controller

We study a generalized setup for learning from demonstration to build an...
11/21/2019 ∙ by Pratyusha Sharma, et al. ∙ 0 ∙ share

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Self-Supervised Exploration via Disagreement

Efficient exploration is a long-standing problem in sensorimotor learnin...
06/10/2019 ∙ by Deepak Pathak, et al. ∙ 0 ∙ share

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Image Inpainting

Fills holes in images

05/17/2019 Deepak Pathak ∙ 29 ∙ share

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Learning to Control Self-Assembling Morphologies: A Study of Generalization via Modularity

Contemporary sensorimotor learning approaches typically start with an ex...
02/14/2019 ∙ by Deepak Pathak, et al. ∙ 12 ∙ share

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Large-Scale Study of Curiosity-Driven Learning

Reinforcement learning algorithms rely on carefully engineering environm...
08/13/2018 ∙ by Yuri Burda, et al. ∙ 0 ∙ share

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Compositional GAN: Learning Conditional Image Composition

Generative Adversarial Networks (GANs) can produce images of surprising ...
07/19/2018 ∙ by Samaneh Azadi, et al. ∙ 16 ∙ share

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Learning Instance Segmentation by Interaction

We present an approach for building an active agent that learns to segme...
06/21/2018 ∙ by Deepak Pathak, et al. ∙ 2 ∙ share

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Zero-Shot Visual Imitation

The current dominant paradigm for imitation learning relies on strong su...
04/23/2018 ∙ by Deepak Pathak, et al. ∙ 0 ∙ share

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Investigating Human Priors for Playing Video Games

What makes humans so good at solving seemingly complex video games? Unli...
02/28/2018 ∙ by Rachit Dubey, et al. ∙ 0 ∙ share

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Toward Multimodal Image-to-Image Translation

Many image-to-image translation problems are ambiguous, as a single inpu...
11/30/2017 ∙ by Jun-Yan Zhu, et al. ∙ 0 ∙ share

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Curiosity-driven Exploration by Self-supervised Prediction

In many real-world scenarios, rewards extrinsic to the agent are extreme...
05/15/2017 ∙ by Deepak Pathak, et al. ∙ 0 ∙ share

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Learning Features by Watching Objects Move

This paper presents a novel yet intuitive approach to unsupervised featu...
12/19/2016 ∙ by Deepak Pathak, et al. ∙ 0 ∙ share

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Context Encoders: Feature Learning by Inpainting

We present an unsupervised visual feature learning algorithm driven by c...
04/25/2016 ∙ by Deepak Pathak, et al. ∙ 0 ∙ share

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Constrained Convolutional Neural Networks for Weakly Supervised Segmentation

We present an approach to learn a dense pixel-wise labeling from image-l...
06/11/2015 ∙ by Deepak Pathak, et al. ∙ 0 ∙ share

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Fully Convolutional Multi-Class Multiple Instance Learning

Multiple instance learning (MIL) can reduce the need for costly annotati...
12/22/2014 ∙ by Deepak Pathak, et al. ∙ 0 ∙ share

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Detector Discovery in the Wild: Joint Multiple Instance and Representation Learning

We develop methods for detector learning which exploit joint training ov...
12/02/2014 ∙ by Judy Hoffman, et al. ∙ 0 ∙ share

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Deepak Pathak

Featured Co-authors

Sergey Levine

Pieter Abbeel

Trevor Darrell

Jitendra Malik

Kate Saenko

Abhinav Gupta

Ross Girshick

Kostas Daniilidis

Alexei A. Efros

Amos Storkey

Thomas L. Griffiths

One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Control

Locally Masked Convolution for Autoregressive Models

Planning to Explore via Self-Supervised World Models

Sparse Graphical Memory for Robust Planning

Third-Person Visual Imitation Learning via Decoupled Hierarchical Controller

Self-Supervised Exploration via Disagreement

Image Inpainting

Learning to Control Self-Assembling Morphologies: A Study of Generalization via Modularity

Large-Scale Study of Curiosity-Driven Learning

Compositional GAN: Learning Conditional Image Composition

Learning Instance Segmentation by Interaction

Zero-Shot Visual Imitation

Investigating Human Priors for Playing Video Games

Toward Multimodal Image-to-Image Translation

Curiosity-driven Exploration by Self-supervised Prediction

Learning Features by Watching Objects Move

Context Encoders: Feature Learning by Inpainting

Constrained Convolutional Neural Networks for Weakly Supervised Segmentation

Fully Convolutional Multi-Class Multiple Instance Learning

Detector Discovery in the Wild: Joint Multiple Instance and Representation Learning