115 citations · 162 across the 14 of their papers we have counts for
6 papers · 1 filter
Learning to Compose SuperWeights for Neural Parameter Allocation Search
Piotr Teterwak, Soren Nelson, Nikoli Dryden +3
Neural parameter allocation search (NPAS) automates parameter sharing by obtaining weights for a network given an arbitrary, fixed parameter budget. Prior work has two major drawba…
CLAMP: Contrastive LAnguage Model Prompt-tuning
Piotr Teterwak, Ximeng Sun, Bryan A. Plummer +2
Large language models (LLMs) have emerged as powerful general-purpose interfaces for many machine learning problems. Recent work has adapted LLMs to generative visual tasks like im…
MixtureGrowth: Growing Neural Networks by Recombining Learned Parameters
Chau Pham, Piotr Teterwak, Soren Nelson +1
Most deep neural networks are trained under fixed network architectures and require retraining when the architecture changes. If expanding the network's size is needed, it is neces…
ERM++: An Improved Baseline for Domain Generalization
Piotr Teterwak, Kuniaki Saito, Theodoros Tsiligkaridis +2
Domain Generalization (DG) aims to develop classifiers that can generalize to new, unseen data distributions, a critical capability when collecting new domain-specific data is impr…
VisDA 2022 Challenge: Domain Adaptation for Industrial Waste Sorting
Dina Bashkirova, Samarth Mishra, Diala Lteif +16
Label-efficient and reliable semantic segmentation is essential for many real-life applications, especially for industrial settings with high visual diversity, such as waste sortin…
Mind the Backbone: Minimizing Backbone Distortion for Robust Object Detection
Kuniaki Saito, Donghyun Kim, Piotr Teterwak +2
Building object detectors that are robust to domain shifts is critical for real-world applications. Prior approaches fine-tune a pre-trained backbone and risk overfitting it to in-…