Jialei Huang, Yang Gao, Jinkun Cao, Ruiqian Nai, Qing Yang
We lifted 2 functions out of this paper's own repositories and ran 1 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 |
|---|---|---|
| noahcao/disentanglement_lib_med | extension | 1 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| pick_by_dis_per_factor | Ran | noahcao/disentanglement_lib_med/disentanglement_lib/evaluation/metrics/med.py code served (permissive licence) · get_code("2383ebfd9c663a11") |
| compute_importance_mi | Not yet run | noahcao/disentanglement_lib_med/disentanglement_lib/evaluation/metrics/med.py code served (permissive licence) · get_code("73ac83469626944c") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Negative-free contrastive learning methods have attracted a lot of attention with simplicity and impressive performances for large-scale pretraining. However, its disentanglement property remains unexplored. In this paper, we examine negative-free contrastive learning methods to study the disentanglement property empirically. We find that existing disentanglement metrics fail to make meaningful measurements for high-dimensional representation models, so we propose a new disentanglement metric based on Mutual Information between latent representations and data factors. With this proposed metric, we benchmark the disentanglement property of negative-free contrastive learning on both popular synthetic datasets and a real-world dataset CelebA. Our study shows that the investigated methods can learn a well-disentangled subset of representation. As far as we know, we are the first to extend the study of disentangled representation learning to highdimensional representation space and introduce negative-free contrastive learning methods into this area. The source code of this paper is available at https: //github.com/noahcao/disentanglement_lib_med.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2206.04756")
get_code_for_paper("2206.04756")
have("2206.04756")
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