Factorized Contrastive Learning: Going Beyond Multi-view Redundancy
Authors: Paul Pu Liang, Zihao Deng, Martin Q. Ma, James Y. Zou, Louis-Philippe Morency, Ruslan Salakhutdinov
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiments We run comprehensive experiments on a suite of synthetic and large-scale real-world datasets with varying requirements of shared and unique task-relevant information, comparing our FACTORCL method to key baselines: |
| Researcher Affiliation | Academia | 1Carnegie Mellon University, 2University of Pennsylvania, 3Stanford University |
| Pseudocode | Yes | Algorithm 1 Standard multimodal CL. |
| Open Source Code | Yes | We release our code and models at https://github.com/pliang279/FactorCL. |
| Open Datasets | Yes | We use a large collection of real-world datasets provided in Multi Bench [45], where we expect varying ratios of shared and unique information important for the task, to compare FACTORCL with other CL baselines: 1. MIMIC [38]: ... 2. MOSEI [93]: ... 3. MOSI [91]: ... 4. UR-FUNNY [27]: ... 5. MUSTARD [12]: ... 6. IRFL [88]: ... |
| Dataset Splits | No | The paper mentions training models on various datasets (e.g., MIMIC, MOSEI, MOSI, UR-FUNNY, MUSTARD, IRFL) and discusses pre-training and fine-tuning, but does not explicitly specify the training, validation, and test dataset splits used for these experiments. |
| Hardware Specification | Yes | All experiments in this paper are run on a single NVIDIA A100 GPU. |
| Software Dependencies | No | The paper mentions using optimizers (Adam) and specific models (CLIP-VIT-B/32) but does not provide specific version numbers for software dependencies like programming languages or libraries (e.g., Python, PyTorch, TensorFlow). |
| Experiment Setup | Yes | We train the model for 100 epochs using the Adam optimizer with a 1e-4 learning rate. |