We lifted 3 functions out of this paper's own repositories and ran 3 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 |
|---|---|---|
| copy not recorded | — | 2 of 2 |
| schatty/siamese-networks-tf | reimplementation | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| preprocess_config | Ran | this paper's copy was not recorded; identical code first harvested from schatty/matching-networks-tf pointer only · get_code("5d3cb59c1da5b053") |
| preprocess_config | Ran | this paper's copy was not recorded; identical code first harvested from schatty/matching-networks-tf pointer only · get_code("53241b134fbef5ed") |
| preprocess_config | Ran | schatty/siamese-networks-tf/scripts/train/run_train.py code served (permissive licence) · get_code("438799a5cf3530b9") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Three years ago, we released the Omniglot dataset for one-shot learning, along with five challenge tasks and a computational model that addresses these tasks. The model was not meant to be the final word on Omniglot; we hoped that the community would build on our work and develop new approaches. In the time since, we have been pleased to see wide adoption of the dataset. There has been notable progress on one-shot classification, but researchers have adopted new splits and procedures that make the task easier. There has been less progress on the other four tasks. We conclude that recent approaches are still far from human-like concept learning on Omniglot, a challenge that requires performing many tasks with a single model.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1902.03477")
get_code_for_paper("1902.03477")
have("1902.03477")
Connect an agent — have() is free.