Phillip Isola

p h i l l i p i @ m i t . e d u
Google Scholar / GitHub / Twitter

About me

I am an associate professor in EECS at MIT studying computer vision, machine learning, and AI.

Previously, I spent a year as a visiting research scientist at OpenAI, and before that I was a postdoctoral scholar with Alyosha Efros in the EECS department at UC Berkeley. I completed my Ph.D. in Brain & Cognitive Sciences at MIT, under the supervision of Ted Adelson, where I also frequently worked with Aude Oliva. I received my undergraduate degree in Computer Science from Yale, where I got my start on research working with Brian Scholl.

Quick links: Papers / Courses / Talks / Research Group


→ We are running a NeurIPS competition on large-scale multiagent learning, on the Neural MMO platform. Check it out here.

Research Group

Our group studies how to make artificial intelligence more like natural intelligence. We are especially interested in intelligence that is embodied, emergent, and general-purpose, all of which are properties that we see in humans and animals.

Topics we currently focus on include representation learning, generative modeling, and multiagent systems. We also enjoy eclectic applications on top of these systems, often in vision and graphics, and also study the misuse of these systems, especially toward spreading misinformation.

The lab is part of the broader Embodied Intelligence and Visual Computing research communities at MIT.

PhD Students
Yen-Chen Lin
Caroline Chan
Lucy Chai
Yonglong Tian
Tongzhou Wang
Joseph Suarez
Minyoung (Jacob) Huh
Hyojin Bahng
Akarsh Kumar
Shobhita Sundaram
MEng Students
Kevin Frans
Stephanie Fu
Swami Sankaranarayanan

Jeff Li
Former Members and Visitors
Dillon Dupont (UROP 2021), Kate Xu (UROP 2021), Maxwell Jiang (UROP 2021), Toru Lin (MEng 2021), Kenny Derek (MEng 2021), Stephanie Fu (UROP 2021), Yilun Du (UROP 2019), Zhongxia Yan (Rotation 2019)


Denoised MDPs: Learning World Models Better Than The World Itself
Tongzhou Wang, Simon S. Du, Antonio Torralba, Phillip Isola, Amy Zhang, Yuandong Tian
ICML 2022.
Any-resolution Training for High-resolution Image Synthesis
Lucy Chai, Michaël Gharbi, Eli Shechtman, Phillip Isola, Richard Zhang
arXiv 2022.
Exploring Visual Prompts for Adapting Large-Scale Models
Hyojin Bahng, Ali Jahanian*, Swami Sankaranarayanan*, Phillip Isola
arXiv 2022.
Learning to generate line drawings that convey geometry and semantics
Caroline Chan, Frédo Durand, Phillip Isola
CVPR 2022.
On the Learning and Learnability of Quasimetrics
Tongzhou Wang, Phillip Isola
ICLR 2022.
Generative Models as a Data Source for Multiview Representation Learning
Ali Jahanian, Xavier Puig, Yonglong Tian, Phillip Isola
ICLR 2022.
[Paper][Website][Code][News article]
NeRF-Supervision: Learning Dense Object Descriptors from Neural Radiance Fields
Lin Yen-Chen, Pete Florence, Jonathan T. Barron, Tsung-Yi Lin, Alberto Rodriguez, Phillip Isola
ICRA 2022.
Learning to Ground Multi-Agent Communication with Autoencoders
Toru Lin, Jacob Huh, Chris Stauffer, Sernam Lim, Phillip Isola
NeurIPS 2021.
The Neural MMO Platform for Massively Multiagent Research
Joseph Suarez, Yilun Du, Clare Zhu, Igor Mordatch, Phillip Isola
NeurIPS 2021, Track on Datasets and Benchmarks.
Adaptable Agent Populations Using a Generative Model of Policies
Kenneth Derek, Phillip Isola
NeurIPS 2021.
Learning to See by Looking at Noise
Manel Baradad*, Jonas Wulff*, Tongzhou Wang, Phillip Isola, Antonio Torralba
NeurIPS 2021 (spotlight).
Explaining in Style: Training a GAN to explain a classifier in StyleSpace
Oran Lang*, Yossi Gandelsman*, Michal Yarom*, Yoav Wald*, Gal Elidan, Avinatan Hassidim, William T. Freeman, Phillip Isola, Amir Globerson, Michal Irani, Inbar Mosseri
ICCV 2021.
Curious Representation Learning for Embodied Intelligence
Yilun Du, Chuang Gan, Phillip Isola
ICCV 2021.
iNeRF: Inverting Neural Radiance Fields for Pose Estimation
Lin Yen-Chen, Pete Florence, Jonathan T. Barron, Alberto Rodriguez, Phillip Isola, Tsung-Yi Lin
IROS 2021.
Ensembling with Deep Generative Views
Lucy Chai, Jun-Yan Zhu, Eli Shechtman, Phillip Isola, Richard Zhang
CVPR 2021.
The Low-Rank Simplicity Bias in Deep Networks
Minyoung Huh, Hossein Mobahi, Richard Zhang, Brian Cheung, Pulkit Agrawal, Phillip Isola
arXiv 2021.
Using latent space regression to analyze and leverage compositionality in GANs
Lucy Chai, Jonas Wulff, Phillip Isola
ICLR 2021.
[Paper][Website][Code][Demo (Colab)]
Noisy Agents: Self-supervised Exploration by Predicting Auditory Events
Chuang Gan, Xiaoyu Chen, Phillip Isola, Antonio Torralba, Joshua B. Tenenbaum
arXiv 2020.
What makes for good views for contrastive learning?
Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, Phillip Isola
NeurIPS 2020.
Supervised Contrastive Learning
Prannay Khosla*, Piotr Teterwak*, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, Dilip Krishnan
NeurIPS 2020.
What makes fake images detectable? Understanding properties that generalize
Lucy Chai, David Bau, Ser-Nam Lim, Phillip Isola
ECCV 2020.
Rethinking Few-Shot Image Classification: A Good Embedding Is All You Need?
Yonglong Tian*, Yue Wang*, Dilip Krishnan, Josh Tenenbaum, Phillip Isola
ECCV 2020.
Contrastive Multiview Coding
Yonglong Tian, Dilip Krishnan, Phillip Isola
ECCV 2020.
Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere
Tongzhou Wang, Phillip Isola
ICML 2020.
Neural MMO v1.3: A Massively Multiagent Game Environment for Training and Evaluating Neural Networks
Joseph Suarez, Yilun Du, Igor Mordatch, Phillip Isola
AAMAS Extended Abstract 2020.
[Extended abstract][arXiv paper][Code]
Learning to See before Learning to Act: Visual Pre-training for Manipulation
Lin Yen-Chen, Andy Zeng, Shuran Song, Phillip Isola, Tsung-Yi Lin
ICRA 2020.
Contrastive Representation Distillation
Yonglong Tian, Dilip Krishnan, Phillip Isola
ICLR 2020.
On the "steerability" of generative adversarial networks
Ali Jahanian*, Lucy Chai*, Phillip Isola
ICLR 2020.

Neural MMO: A Massively Multiagent Game Environment for Training and Evaluating Intelligent Agents
Joseph Suarez, Yilun Du, Phillip Isola, Igor Mordatch
arXiv 2019.
Learning to Control Self-Assembling Morphologies: A Study of Generalization via Modularity
Deepak Pathak*, Chris Lu*, Trevor Darrell, Phillip Isola, Alexei A. Efros
NeurIPS 2019.
Winner of Virtual Creatures Competition at GECCO 2019 (link)
GANalyze: Toward Visual Definitions of Cognitive Image Properties
Lore Goetschalckx*, Alex Andonian, Aude Oliva, Phillip Isola
ICCV 2019.
InGAN: Capturing and Remapping the "DNA" of a Natural Image
Assaf Shocher, Shai Bagon, Phillip Isola, Michal Irani
ICCV 2019 (oral).
Experience-embedded Visual Foresight
Lin Yen-Chen, Maria Bauza, Phillip Isola
CoRL 2019.
Omnipush: accurate, diverse, real-world dataset of pushing dynamics with RGBD images
Maria Bauza, Ferran Alet, Yen-Chen Lin, Tomas Lozano-Perez, Leslie P. Kaelbling, Phillip Isola, Alberto Rodriguez
IROS 2019.

Evolved Policy Gradients
Rein Houthooft, Richard Y. Chen, Phillip Isola, Bradly C. Stadie, Filip Wolski, Jonathan Ho, Pieter Abbeel
NeurIPS 2018.
CyCADA: Cycle-Consistent Adversarial Domain Adaptation
Judy Hoffman, Eric Tzeng, Taesung Park, Jun-Yan Zhu, Phillip Isola, Kate Saenko, Alexei A. Efros, Trevor Darrell
ICML 2018.
Memorable words are monogamous: The role of synonymy and homonymy in word recognition memory
Kyle Mahowald*, Phillip Isola*, Evelina Fedorenko, Edward Gibson, Aude Oliva
PsyArXiv 2018.
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, Oliver Wang
CVPR 2018.
What You Sketch Is What You Get: 3D Sketching using Multi-View Deep Volumetric Prediction
Johanna Delanoy, Adrien Bousseau, Mathieu Aubry, Phillip Isola, Alexei A. Efros
I3D 2018.

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
Jun-Yan Zhu*, Taesung Park*, Phillip Isola, Alexei A. Efros
ICCV 2017.
Combining Self-Supervised Learning and Imitation for Vision-Based Rope Manipulation
Ashvin Nair, Pulkit Agrawal, Dian Chen, Phillip Isola, Pieter Abbeel, Jitendra Malik, Sergey Levine
ICRA 2017.
Richard Zhang*, Jun-Yan Zhu*, Phillip Isola, Xinyang Geng, Angela S. Lin, Tianhe Yu, Alexei A. Efros
Real-Time User-Guided Image Colorization with Learned Deep Priors
Ali Jahanian, Phillip Isola, Donglai Wei
Mining Visual Evolution in 21 Years of Web Design
CHI Extended Abstract, 2017.
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, Alexei A. Efros
Image-to-Image Translation with Conditional Adversarial Networks
CVPR, 2017.
Richard Zhang, Phillip Isola, Alexei A. Efros
Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction
CVPR, 2017.

Richard Zhang, Phillip Isola, Alexei A. Efros
Colorful Image Colorization
European Conference on Computer Vision (ECCV), 2016 (oral).
Andrew Owens, Phillip Isola, Josh McDermott, Antonio Torralba, Edward H. Adelson, William T. Freeman
Visually Indicated Sounds
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 (oral).
[Paper][Website][Video][Dataset (50GB)]
Phillip Isola, Daniel Zoran, Dilip Krishnan, Edward H. Adelson
Learning Visual Groups From Co-occurrences in Space and Time
International Conference on Learning Representations, Workshop paper, 2016

Daniel Zoran, Phillip Isola, Dilip Krishnan, William T. Freeman
Learning Ordinal Relationships for Mid-Level Vision
International Conference on Computer Vision (ICCV), 2015.
Phillip Isola*, Joseph J. Lim*, Edward H. Adelson
Discovering States and Transformations in Image Collections
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 (oral).
Zoya Bylinskii, Phillip Isola, Constance Bainbridge, Antonio Torralba, Aude Oliva
Intrinsic and Extrinsic Effects on Image Memorability
Vision Research, 2015.
Zhengdong Zhang, Phillip Isola, Edward H. Adelson
Sparkle Vision: Seeing the World through Random Specular Microfacets
4th IEEE International Workshop on Computational Cameras and Displays, 2015.

Phillip Isola, Daniel Zoran, Dilip Krishnan, Edward H. Adelson
Crisp Boundary Detection Using Pointwise Mutual Information
European Conference on Computer Vision (ECCV), 2014 (oral).

Phillip Isola, Jianxiong Xiao, Devi Parikh, Antonio Torralba, Aude Oliva
What Makes a Photograph Memorable?
Pattern Analysis and Machine Intelligence (PAMI), 2013.
Phillip Isola and Ce Liu
Scene Collaging: Analysis and Synthesis of Natural Images with Semantic Layers.
International Conference on Computer Vision (ICCV), 2013.
Michelle Borkin, Azalea Vo, Zoya Bylinskii, Phillip Isola, Shashank Sunkavalli, Aude Oliva, Hanspeter Pfister
What Makes a Visualization Memorable?
IEEE Transactions on Visualization and Computer Graphics (Infovis), 2013.
Wilma A. Bainbridge, Phillip Isola, Aude Oliva
The Intrinsic Memorability of Face Images.
Journal of Experimental Psychology: General, 2013.
Aude Oliva, Phillip Isola, Aditya Khosla, Wilma A. Bainbridge
What makes a picture memorable?
SPIE Newsroom Article, 2013.

Aditya Khosla*, Jianxiong Xiao*, Phillip Isola, Antonio Torralba, Aude Oliva
Image Memorability and Visual Inception.
SIGGRAPH Asia Technical Briefs (Invited Paper), 2012.
Forrester Cole, Phillip Isola, William T. Freeman, Fredo Durand, Edward H. Adelson
Shapecollage: Occlusion-aware, example-based shape interpretation.
European Conference on Computer Vision (ECCV), 2012.
Wilma A. Bainbridge*, Phillip Isola*, Idan Blank, Aude Oliva
Establishing a database for studying human face photograph memorability.
Proceedings of the Annual Conference of the Cognitive Sciences Society, 2012.

Phillip Isola, Devi Parikh, Antonio Torralba, Aude Oliva
Understanding the intrinsic memorability of images.
NeurIPS, 2011.
Phillip Isola, Jianxiong Xiao, Antonio Torralba, Aude Oliva
What makes an image memorable?
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011.

Nicholas B. Turk-Browne, Phillip Isola, Brian J. Scholl, Teresa A. Treat
Multidimensional visual statistical learning.
Journal of Experimental Psychology: Learning, Memory, and Cognition, 2008.