Freddie Smith, Xiaoliang Luo, Brett Roads, Bradley Love
We lifted 1 functions out of this paper's own repositories and ran 0 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| fbickfordsmith/attention-iclr | canonical | 0 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| attention_network | Not yet run | fbickfordsmith/attention-iclr/attention/utils/models.py code served (permissive licence) · get_code("cebe6c628e38005f") |
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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 a large-scale computational experiment. First, we design a collection of visual tasks, each consisting of classifying images from a chosen task set (subset of ImageNet categories). The nature of a task is determined by which categories are included in the task set. Second, on each task we train an attention-augmented neural network and then compare its accuracy to that of a baseline network. We show that the perceptual boost of attention is stronger with increasing task-set difficulty, weaker with increasing task-set size and weaker with increasing perceptual similarity within a task set.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2003.00882")
get_code_for_paper("2003.00882")
have("2003.00882")
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