Search Results for author: Rebecca Roelofs

Found 18 papers, 8 papers with code

When does dough become a bagel? Analyzing the remaining mistakes on ImageNet

no code implementations9 May 2022 Vijay Vasudevan, Benjamin Caine, Raphael Gontijo-Lopes, Sara Fridovich-Keil, Rebecca Roelofs

To help contextualize progress on ImageNet and provide a more meaningful evaluation for today's state-of-the-art models, we manually review and categorize every remaining mistake that a few top models make in order to provide insight into the long-tail of errors on one of the most benchmarked datasets in computer vision.

Image Classification

Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time

2 code implementations10 Mar 2022 Mitchell Wortsman, Gabriel Ilharco, Samir Yitzhak Gadre, Rebecca Roelofs, Raphael Gontijo-Lopes, Ari S. Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, Ludwig Schmidt

In this paper, we revisit the second step of this procedure in the context of fine-tuning large pre-trained models, where fine-tuned models often appear to lie in a single low error basin.

 Ranked #1 on Image Classification on ImageNet V2 (using extra training data)

Domain Generalization Image Classification +1

Spectral Bias in Practice: The Role of Function Frequency in Generalization

no code implementations6 Oct 2021 Sara Fridovich-Keil, Raphael Gontijo-Lopes, Rebecca Roelofs

We also explore the connections between function frequency and image frequency and find that spectral bias is sensitive to the low frequencies prevalent in natural images.

Data Augmentation Image Classification

Robust fine-tuning of zero-shot models

1 code implementation4 Sep 2021 Mitchell Wortsman, Gabriel Ilharco, Jong Wook Kim, Mike Li, Simon Kornblith, Rebecca Roelofs, Raphael Gontijo Lopes, Hannaneh Hajishirzi, Ali Farhadi, Hongseok Namkoong, Ludwig Schmidt

Compared to standard fine-tuning, WiSE-FT provides large accuracy improvements under distribution shift, while preserving high accuracy on the target distribution.

Transfer Learning

The Evolution of Out-of-Distribution Robustness Throughout Fine-Tuning

no code implementations30 Jun 2021 Anders Andreassen, Yasaman Bahri, Behnam Neyshabur, Rebecca Roelofs

Although machine learning models typically experience a drop in performance on out-of-distribution data, accuracies on in- versus out-of-distribution data are widely observed to follow a single linear trend when evaluated across a testbed of models.

AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation

5 code implementations ICLR 2022 David Berthelot, Rebecca Roelofs, Kihyuk Sohn, Nicholas Carlini, Alex Kurakin

We extend semi-supervised learning to the problem of domain adaptation to learn significantly higher-accuracy models that train on one data distribution and test on a different one.

Unsupervised Domain Adaptation

Pseudo-labeling for Scalable 3D Object Detection

no code implementations2 Mar 2021 Benjamin Caine, Rebecca Roelofs, Vijay Vasudevan, Jiquan Ngiam, Yuning Chai, Zhifeng Chen, Jonathon Shlens

To safely deploy autonomous vehicles, onboard perception systems must work reliably at high accuracy across a diverse set of environments and geographies.

3D Object Detection Autonomous Vehicles +2

Mitigating Bias in Calibration Error Estimation

1 code implementation15 Dec 2020 Rebecca Roelofs, Nicholas Cain, Jonathon Shlens, Michael C. Mozer

We find that binning-based estimators with bins of equal mass (number of instances) have lower bias than estimators with bins of equal width.

A Meta-Analysis of Overfitting in Machine Learning

no code implementations NeurIPS 2019 Rebecca Roelofs, Vaishaal Shankar, Benjamin Recht, Sara Fridovich-Keil, Moritz Hardt, John Miller, Ludwig Schmidt

By systematically comparing the public ranking with the final ranking, we assess how much participants adapted to the holdout set over the course of a competition.

Do Image Classifiers Generalize Across Time?

1 code implementation ICCV 2021 Vaishaal Shankar, Achal Dave, Rebecca Roelofs, Deva Ramanan, Benjamin Recht, Ludwig Schmidt

Additionally, we evaluate three detection models and show that natural perturbations induce both classification as well as localization errors, leading to a median drop in detection mAP of 14 points.

14 General Classification +1

A Systematic Framework for Natural Perturbations from Videos

no code implementations ICML Workshop Deep_Phenomen 2019 Vaishaal Shankar, Achal Dave, Rebecca Roelofs, Deva Ramanan, Benjamin Recht, Ludwig Schmidt

We introduce a systematic framework for quantifying the robustness of classifiers to naturally occurring perturbations of images found in videos.

14 Video Object Detection

Do CIFAR-10 Classifiers Generalize to CIFAR-10?

3 code implementations1 Jun 2018 Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, Vaishaal Shankar

Although we ensure that the new test set is as close to the original data distribution as possible, we find a large drop in accuracy (4% to 10%) for a broad range of deep learning models.

The Marginal Value of Adaptive Gradient Methods in Machine Learning

2 code implementations NeurIPS 2017 Ashia C. Wilson, Rebecca Roelofs, Mitchell Stern, Nathan Srebro, Benjamin Recht

Adaptive optimization methods, which perform local optimization with a metric constructed from the history of iterates, are becoming increasingly popular for training deep neural networks.

Large Scale Kernel Learning using Block Coordinate Descent

no code implementations17 Feb 2016 Stephen Tu, Rebecca Roelofs, Shivaram Venkataraman, Benjamin Recht

We demonstrate that distributed block coordinate descent can quickly solve kernel regression and classification problems with millions of data points.

Classification General Classification

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