36 citations · 197 across the 20 of their papers we have counts for
11 papers · 1 filter
How Efficient Are Today's Continual Learning Algorithms?
Md Yousuf Harun, Jhair Gallardo, Tyler L. Hayes +1
Supervised Continual learning involves updating a deep neural network (DNN) from an ever-growing stream of labeled data. While most work has focused on overcoming catastrophic forg…
SIESTA: Efficient Online Continual Learning with Sleep
Md Yousuf Harun, Jhair Gallardo, Tyler L. Hayes +2
In supervised continual learning, a deep neural network (DNN) is updated with an ever-growing data stream. Unlike the offline setting where data is shuffled, we cannot make any dis…
Can I see an Example? Active Learning the Long Tail of Attributes and Relations
Tyler L. Hayes, Maximilian Nickel, Christopher Kanan +2
There has been significant progress in creating machine learning models that identify objects in scenes along with their associated attributes and relationships; however, there is…
Disentangling Transfer and Interference in Multi-Domain Learning
Yipeng Zhang, Tyler L. Hayes, Christopher Kanan
Humans are incredibly good at transferring knowledge from one domain to another, enabling rapid learning of new tasks. Likewise, transfer learning has enabled enormous success in m…
Self-Supervised Training Enhances Online Continual Learning
Jhair Gallardo, Tyler L. Hayes, Christopher Kanan
In continual learning, a system must incrementally learn from a non-stationary data stream without catastrophic forgetting. Recently, multiple methods have been devised for increme…
Improved Robustness to Open Set Inputs via Tempered Mixup
Ryne Roady, Tyler L. Hayes, Christopher Kanan
Supervised classification methods often assume that evaluation data is drawn from the same distribution as training data and that all classes are present for training. However, rea…
RODEO: Replay for Online Object Detection
Manoj Acharya, Tyler L. Hayes, Christopher Kanan
Humans can incrementally learn to do new visual detection tasks, which is a huge challenge for today's computer vision systems. Incrementally trained deep learning models lack back…
Do We Need Fully Connected Output Layers in Convolutional Networks?
Zhongchao Qian, Tyler L. Hayes, Kushal Kafle +1
Traditionally, deep convolutional neural networks consist of a series of convolutional and pooling layers followed by one or more fully connected (FC) layers to perform the final c…
Are Out-of-Distribution Detection Methods Effective on Large-Scale Datasets?
Ryne Roady, Tyler L. Hayes, Ronald Kemker +2
Supervised classification methods often assume the train and test data distributions are the same and that all classes in the test set are present in the training set. However, dep…
Compassionately Conservative Balanced Cuts for Image Segmentation
Nathan D. Cahill, Tyler L. Hayes, Renee T. Meinhold +1
The Normalized Cut (NCut) objective function, widely used in data clustering and image segmentation, quantifies the cost of graph partitioning in a way that biases clusters or segm…
Efficiently Computing Piecewise Flat Embeddings for Data Clustering and Image Segmentation
Renee T. Meinhold, Tyler L. Hayes, Nathan D. Cahill
Image segmentation is a popular area of research in computer vision that has many applications in automated image processing. A recent technique called piecewise flat embeddings (P…