Yann Lecun, Adrien Bardes, Jean Ponce, Convolutional Encoder
We lifted 20 functions out of this paper's own repositories and ran 16 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 |
|---|---|---|
| facebookresearch/VICRegL | canonical | 3 of 3 |
| lightly-ai/lightly | — | 5 of 7 |
| facebookresearch/vicregl | — | 5 of 7 |
| Futurne/vicreg-loss | — | 2 of 2 |
| copy not recorded | — | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| GatherLayer | Ran | lightly-ai/lightly/lightly/loss/vicregl_loss.py code served (permissive licence) · get_code("ba565bd2bd01c209") |
| MLP | Ran | facebookresearch/vicregl/main_vicregl.py pointer only (licence: NOASSERTION) · get_code("0393d6f458d1bd7e") |
| VICRegLLoss | Ran | Futurne/vicreg-loss/vicreg_loss/vicregl.py pointer only (licence: NONE) · get_code("813497ec5b533d79") |
| VICRegLoss | Ran | Futurne/vicreg-loss/vicreg_loss/vicregl.py pointer only (licence: NONE) · get_code("43e2587f6349819b") |
| VICRegLoss | Ran | lightly-ai/lightly/lightly/loss/vicregl_loss.py code served (permissive licence) · get_code("90c615a3b586fa8c") |
| accuracy | Ran | this paper's copy was not recorded; identical code first harvested from facebookresearch/barlowtwins pointer only · get_code("b0f936d4d6ae3b8c") |
| accuracy | Ran | facebookresearch/vicregl/main_vicregl.py pointer only (licence: NOASSERTION) · get_code("f59a1c321043b890") |
| batch_all_gather | Ran | facebookresearch/vicregl/main_vicregl.py pointer only (licence: NOASSERTION) · get_code("e413e4905a6a548b") |
| batched_index_select | Ran | facebookresearch/VICRegL/main_vicregl.py pointer only (licence: NOASSERTION) · get_code("1345f4fc63011f70") |
| covariance_loss | Ran | lightly-ai/lightly/lightly/loss/vicregl_loss.py code served (permissive licence) · get_code("15d4e6fc5c5683f1") |
| invariance_loss | Ran | lightly-ai/lightly/lightly/loss/vicregl_loss.py code served (permissive licence) · get_code("a20e1319851797c7") |
| make_inputs | Ran | facebookresearch/VICRegL/main_vicregl.py pointer only (licence: NOASSERTION) · get_code("62036ceee17bee5d") |
| neirest_neighbores | Ran | facebookresearch/VICRegL/main_vicregl.py pointer only (licence: NOASSERTION) · get_code("d53b48478f98d717") |
| neirest_neighbores_on_l2 | Ran | facebookresearch/vicregl/main_vicregl.py pointer only (licence: NOASSERTION) · get_code("80bfa31403ede22a") |
| neirest_neighbores_on_location | Ran | facebookresearch/vicregl/main_vicregl.py pointer only (licence: NOASSERTION) · get_code("8e775945d18d2299") |
| variance_loss | Ran | lightly-ai/lightly/lightly/loss/vicregl_loss.py code served (permissive licence) · get_code("44598859a9de393b") |
| VICRegL | Not yet run | facebookresearch/vicregl/main_vicregl.py pointer only (licence: NOASSERTION) · get_code("bbe9eaa0bec2c145") |
| VICRegLLoss | Not yet run | lightly-ai/lightly/lightly/loss/vicregl_loss.py code served (permissive licence) · get_code("6fc75c85f0b33a2c") |
| gather | Not yet run | lightly-ai/lightly/lightly/loss/vicregl_loss.py code served (permissive licence) · get_code("f60f882797a1d561") |
| gather_center | Not yet run | facebookresearch/vicregl/main_vicregl.py pointer only (licence: NOASSERTION) · get_code("7fe4deeb043278d7") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Most recent self-supervised methods for learning image representations focus on either producing a global feature with invariance properties, or producing a set of local features. The former works best for classification tasks while the latter is best for detection and segmentation tasks. This paper explores the fundamental tradeoff between learning local and global features. A new method called VICRegL is proposed that learns good global and local features simultaneously, yielding excellent performance on detection and segmentation tasks while maintaining good performance on classification tasks. Concretely, two identical branches of a standard convolutional net architecture are fed two differently distorted versions of the same image. The VICReg criterion is applied to pairs of global feature vectors. Simultaneously, the VICReg criterion is applied to pairs of local feature vectors occurring before the last pooling layer. Two local feature vectors are attracted to each other if their l 2 -distance is below a threshold or if their relative locations are consistent with a known geometric transformation between the two input images. We demonstrate strong performance on linear classification and segmentation transfer tasks. Code and pretrained models are publicly available at: https://github.com/facebookresearch/VICRegL
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2210.01571")
get_code_for_paper("2210.01571")
have("2210.01571")
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