1 citations · 1 across the 4 of their papers we have counts for
4 papers
What could go wrong? Discovering and describing failure modes in computer vision
Gabriela Csurka, Tyler L. Hayes, Diane Larlus +1
Deep learning models are effective, yet brittle. Even carefully trained, their behavior tends to be hard to predict when confronted with out-of-distribution samples. In this work,…
PANDAS: Prototype-based Novel Class Discovery and Detection
Tyler L. Hayes, César R. de Souza, Namil Kim +3
Object detectors are typically trained once and for all on a fixed set of classes. However, this closed-world assumption is unrealistic in practice, as new classes will inevitably…
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…
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…