Piotr Teterwak

According to our database1, Piotr Teterwak authored at least 15 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Article 
PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
SLANT: Spurious Logo ANalysis Toolkit.
CoRR, 2024

Vision-LLMs Can Fool Themselves with Self-Generated Typographic Attacks.
CoRR, 2024

Learning to Compose SuperWeights for Neural Parameter Allocation Search.
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2024

MixtureGrowth: Growing Neural Networks by Recombining Learned Parameters.
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2024

2023
CLAMP: Contrastive LAnguage Model Prompt-tuning.
CoRR, 2023

ERM++: An Improved Baseline for Domain Generalization.
CoRR, 2023

Mind the Backbone: Minimizing Backbone Distortion for Robust Object Detection.
CoRR, 2023

2021
VisDA-2021 Competition Universal Domain Adaptation to Improve Performance on Out-of-Distribution Data.
CoRR, 2021


VisDA-2021 Competition: Universal Domain Adaptation to Improve Performance on Out-of-Distribution Data.
Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track, 2021

Understanding Invariance via Feedforward Inversion of Discriminatively Trained Classifiers.
Proceedings of the 38th International Conference on Machine Learning, 2021

Tune it the Right Way: Unsupervised Validation of Domain Adaptation via Soft Neighborhood Density.
Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

OCONet: Image Extrapolation by Object Completion.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

2020
Supervised Contrastive Learning.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019
Boundless: Generative Adversarial Networks for Image Extension.
Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019


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