115 citations · 162 across the 14 of their papers we have counts for
14 papers
OP-LoRA: The Blessing of Dimensionality
Piotr Teterwak, Kate Saenko, Bryan A. Plummer +1
Low-rank adapters (LoRA) enable finetuning of large models with only a small number of parameters. However, they often suffer from an ill-conditioned loss landscape, leading to dif…
Is Large-Scale Pretraining the Secret to Good Domain Generalization?
Piotr Teterwak, Kuniaki Saito, Theodoros Tsiligkaridis +2
Multi-Source Domain Generalization (DG) is the task of training on multiple source domains and achieving high classification performance on unseen target domains. Recent methods co…
SLANT: Spurious Logo ANalysis Toolkit
Maan Qraitem, Piotr Teterwak, Kate Saenko +1
Online content is filled with logos, from ads and social media posts to website branding and product placements. Consequently, these logos are prevalent in the extensive web-scrape…
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-…
Tune it the Right Way: Unsupervised Validation of Domain Adaptation via Soft Neighborhood Density
Kuniaki Saito, Donghyun Kim, Piotr Teterwak +3
Unsupervised domain adaptation (UDA) methods can dramatically improve generalization on unlabeled target domains. However, optimal hyper-parameter selection is critical to achievin…
VisDA-2021 Competition Universal Domain Adaptation to Improve Performance on Out-of-Distribution Data
Dina Bashkirova, Dan Hendrycks, Donghyun Kim +5
Progress in machine learning is typically measured by training and testing a model on the same distribution of data, i.e., the same domain. This over-estimates future accuracy on o…
Understanding Invariance via Feedforward Inversion of Discriminatively Trained Classifiers
Piotr Teterwak, Chiyuan Zhang, Dilip Krishnan +1
A discriminatively trained neural net classifier can fit the training data perfectly if all information about its input other than class membership has been discarded prior to the…
Supervised Contrastive Learning
Prannay Khosla, Piotr Teterwak, Chen Wang +6
Contrastive learning applied to self-supervised representation learning has seen a resurgence in recent years, leading to state of the art performance in the unsupervised training…
Boundless: Generative Adversarial Networks for Image Extension
Piotr Teterwak, Aaron Sarna, Dilip Krishnan +4
Image extension models have broad applications in image editing, computational photography and computer graphics. While image inpainting has been extensively studied in the literat…