imantdaunhawer/multimodal-contrastive-learning
[ICLR 2023] Official code for the paper "Identifiability Results for Multimodal Contrastive Learning"
This is a research project providing code for deep learning researchers. It takes in structured numerical data or image/text pairs and trains models to identify distinct underlying factors from multimodal data. The output is a trained model and evaluation results, useful for researchers studying representation learning and multimodal data analysis.
No commits in the last 6 months.
Use this if you are a machine learning researcher exploring the theoretical underpinnings and practical application of multimodal contrastive learning for identifiability.
Not ideal if you are a practitioner looking for a ready-to-use tool for general-purpose multimodal data analysis without deep expertise in machine learning research.
Stars
35
Forks
4
Language
Python
License
—
Category
Last pushed
Mar 17, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/imantdaunhawer/multimodal-contrastive-learning"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
AdaptiveMotorControlLab/CEBRA
Learnable latent embeddings for joint behavioral and neural analysis - Official implementation of CEBRA
theolepage/sslsv
Toolkit for training and evaluating Self-Supervised Learning (SSL) frameworks for Speaker...
PaddlePaddle/PASSL
PASSL包含 SimCLR，MoCo v1/v2，BYOL，CLIP，PixPro，simsiam, SwAV, BEiT，MAE 等图像自监督算法以及 Vision...
YGZWQZD/LAMDA-SSL
30 Semi-Supervised Learning Algorithms
ModSSC/ModSSC
ModSSC: A Modular Framework for Semi Supervised Classification