Jan Van Gemert, Silvia Pintea, Yancong Lin, Jouke Dijkstra
We lifted 14 functions out of this paper's own repositories and ran 14 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 |
|---|---|---|
| silvialaurapintea/reg-cls | canonical | 14 of 14 |
| Function | Status | Where it lives |
|---|---|---|
| accuracy | Ran | silvialaurapintea/reg-cls/1d/utils.py pointer only (licence: NONE) · get_code("17fb0539d6b732dd") |
| change_ranges | Ran | silvialaurapintea/reg-cls/1d/histeq.py pointer only (licence: NONE) · get_code("21fe9de526aff373") |
| conv3x3 | Ran | silvialaurapintea/reg-cls/images/imdb-wiki-dir/resnet.py pointer only (licence: NONE) · get_code("fac5364e2f53c6db") |
| def_dataloaders | Ran | silvialaurapintea/reg-cls/1d/train_eval.py pointer only (licence: NONE) · get_code("2737bfb410c85be7") |
| doEq | Ran | silvialaurapintea/reg-cls/1d/histeq.py pointer only (licence: NONE) · get_code("713b88d78142fdf6") |
| func | Ran | silvialaurapintea/reg-cls/1d/make_data1d.py pointer only (licence: NONE) · get_code("af188df6229ac760") |
| get_imbalanced | Ran | silvialaurapintea/reg-cls/1d/make_data1d.py pointer only (licence: NONE) · get_code("d149be207b41dae6") |
| get_uniform | Ran | silvialaurapintea/reg-cls/1d/make_data1d.py pointer only (licence: NONE) · get_code("c6c46e08e8618f36") |
| removeStartZeros | Ran | silvialaurapintea/reg-cls/1d/histeq.py pointer only (licence: NONE) · get_code("6e97647f8b72f5a8") |
| resume | Ran | silvialaurapintea/reg-cls/1d/train_eval.py pointer only (licence: NONE) · get_code("9d857dee26c989e2") |
| shot_metrics | Ran | silvialaurapintea/reg-cls/images/imdb-wiki-dir/train_cls.py pointer only (licence: NONE) · get_code("6e71bc6cd8ccb243") |
| weighted_focal_mse_loss | Ran | silvialaurapintea/reg-cls/images/imdb-wiki-dir/loss.py pointer only (licence: NONE) · get_code("953f661a54a1b2bd") |
| weighted_l1_loss | Ran | silvialaurapintea/reg-cls/images/imdb-wiki-dir/loss.py pointer only (licence: NONE) · get_code("2a25dd59364377ff") |
| weighted_mse_loss | Ran | silvialaurapintea/reg-cls/images/imdb-wiki-dir/loss.py pointer only (licence: NONE) · get_code("1da83cb2ed411cd0") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
A number of computer vision deep regression approaches report improved results when adding a classification loss to the regression loss. Here, we explore why this is useful in practice and when it is beneficial. To do so, we start from precisely controlled dataset variations and data samplings and find that the effect of adding a classification loss is the most pronounced for regression with imbalanced data. We explain these empirical findings by formalizing the relation between the balanced and imbalanced regression losses. Finally, we show that our findings hold on two real imbalanced image datasets for depth estimation (NYUD2-DIR), and age estimation (IMDB-WIKI-DIR), and on the problem of imbalanced video progress prediction (Breakfast). Our main takeaway is: for a regression task, if the data sampling is imbalanced, then add a classification loss.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2308.10603")
get_code_for_paper("2308.10603")
have("2308.10603")
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