We lifted 15 functions out of this paper's own repositories and ran 4 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 |
|---|---|---|
| eify/open_clip | canonical | 4 of 15 |
| Function | Status | Where it lives |
|---|---|---|
| convert_to_custom_text_state_dict | Ran | eify/open_clip/src/open_clip/model.py pointer only (licence: NONE) · get_code("f19962ebb134b3d7") |
| get_1d_sincos_pos_embed_from_grid | Ran | eify/open_clip/src/open_clip/pos_embed.py pointer only (licence: NONE) · get_code("4bdd36eab04c3f62") |
| get_cast_dtype | Ran | eify/open_clip/src/open_clip/model.py pointer only (licence: NONE) · get_code("dcd422d66b0581d8") |
| get_input_dtype | Ran | eify/open_clip/src/open_clip/model.py pointer only (licence: NONE) · get_code("b476c8cfbf0f1f47") |
| gather_features | Not yet run | eify/open_clip/src/open_clip/loss.py pointer only (licence: NONE) · get_code("ddcbd45e940484ee") |
| get_2d_sincos_pos_embed | Not yet run | eify/open_clip/src/open_clip/pos_embed.py pointer only (licence: NONE) · get_code("9417ae492629cf06") |
| get_2d_sincos_pos_embed_from_grid | Not yet run | eify/open_clip/src/open_clip/pos_embed.py pointer only (licence: NONE) · get_code("4fd80de79832745d") |
| get_model_config | Not yet run | eify/open_clip/src/open_clip/factory.py pointer only (licence: NOASSERTION) · get_code("491eae38899d6610") |
| load_openai_model | Not yet run | eify/open_clip/src/open_clip/openai.py pointer only (licence: NOASSERTION) · get_code("8811c5d06f3a2eaa") |
| load_state_dict | Not yet run | eify/open_clip/src/open_clip/factory.py pointer only (licence: NONE) · get_code("ef8f5bfd8696abff") |
| neighbour_exchange | Not yet run | eify/open_clip/src/open_clip/loss.py pointer only (licence: NONE) · get_code("e332856e3c2fc814") |
| neighbour_exchange_bidir | Not yet run | eify/open_clip/src/open_clip/loss.py pointer only (licence: NONE) · get_code("5b1fd364afcf3c05") |
| parse_model_name | Not yet run | eify/open_clip/src/open_clip/factory.py pointer only (licence: NONE) · get_code("898cc6c8249c06a0") |
| prepare_inputs_for_generation | Not yet run | eify/open_clip/src/open_clip/coca_model.py pointer only (licence: NONE) · get_code("fb651d0a97fd4d3f") |
| register_pooler | Not yet run | eify/open_clip/src/open_clip/hf_model.py pointer only (licence: NONE) · get_code("2a377da4a76a2d44") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Since the publication of CLIP, the approach of using InfoNCE loss for contrastive pre-training has become widely popular for bridging two or more modalities. Despite its wide adoption, CLIP's original design choices of L2 normalization and cosine similarity logit have rarely been revisited. We have systematically experimented with alternative geometries and softmax logits for language-image pre-training and identified that variants with intuitive Euclidean geometry, Euclidean CLIP (EuCLIP), match or exceed the performance of CLIP and support hierarchical relationships at least as well as more complicated hyperbolic alternative.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2409.13079")
get_code_for_paper("2409.13079")
have("2409.13079")
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