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 | — | 3 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| identity | Ran | this paper's copy was not recorded; identical code first harvested from lucidrains/recurrent-memory-transformer-pytorch pointer only · get_code("7f1040f5e3991d5e") |
| default | Ran | this paper's copy was not recorded; identical code first harvested from ThomasMrY/VCT pointer only · get_code("60fff7c3c400d7ff") |
| exists | Ran | this paper's copy was not recorded; identical code first harvested from ThomasMrY/VCT pointer only · get_code("aa5486a3650902d8") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Recently, large-scale Contrastive Language-Image Pre-training (CLIP) has attracted unprecedented attention for its impressive zero-shot recognition ability and excellent transferability to downstream tasks. However, CLIP is quite data-hungry and requires 400M image-text pairs for pre-training, thereby restricting its adoption. This work proposes a novel training paradigm, Data efficient CLIP (DeCLIP), to alleviate this limitation. We demonstrate that by carefully utilizing the widespread supervision among the image-text pairs, our De-CLIP can learn generic visual features more efficiently. Instead of using the single image-text contrastive supervision, we fully exploit data potential through the use of (1) self-supervision within each modality; (2) multi-view supervision across modalities; (3) nearest-neighbor supervision from other similar pairs. Benefiting from intrinsic supervision, our DeCLIP-ResNet50 can achieve 60.4% zero-shot top1 accuracy on ImageNet, which is 0.8% above the CLIP-ResNet50 while using 7.1 x fewer data. Our DeCLIP-ResNet50 outperforms its counterpart in 8 out of 11 visual datasets when transferred to downstream tasks. Moreover, Scaling up the model and computing also works well in our framework.Our code, dataset and models are released at: https://github.com/Sense-GVT/DeCLIP
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2110.05208")
get_code_for_paper("2110.05208")
have("2110.05208")
Connect an agent — have() is free.