134 citations · 331 across the 15 of their papers we have counts for
5 papers · 1 filter
Do Invariances in Deep Neural Networks Align with Human Perception?
Vedant Nanda, Ayan Majumdar, Camila Kolling +4
An evaluation criterion for safe and trustworthy deep learning is how well the invariances captured by representations of deep neural networks (DNNs) are shared with humans. We ide…
Understanding top-down attention using task-oriented ablation design
Freddie Bickford Smith, Brett D Roads, Xiaoliang Luo +1
Top-down attention allows neural networks, both artificial and biological, to focus on the information most relevant for a given task. This is known to enhance performance in visua…
A Too-Good-to-be-True Prior to Reduce Shortcut Reliance
Nikolay Dagaev, Brett D. Roads, Xiaoliang Luo +3
Despite their impressive performance in object recognition and other tasks under standard testing conditions, deep networks often fail to generalize to out-of-distribution (o.o.d.)…
Enriching ImageNet with Human Similarity Judgments and Psychological Embeddings
Brett D. Roads, Bradley C. Love
Advances in object recognition flourished in part because of the availability of high-quality datasets and associated benchmarks. However, these benchmarks---such as ILSVRC---are r…
The perceptual boost of visual attention is task-dependent in naturalistic settings
Freddie Bickford Smith, Xiaoliang Luo, Brett D. Roads +1
Top-down attention allows people to focus on task-relevant visual information. Is the resulting perceptual boost task-dependent in naturalistic settings? We aim to answer this with…