Removing Bias in Multi-modal Classifiers: Regularization by Maximizing Functional Entropies
Authors: Itai Gat, Idan Schwartz, Alexander Schwing, Tamir Hazan
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On the two challenging multi-modal datasets VQA-CPv2 and Social IQ, we obtain state-of-the-art results while more uniformly exploiting the modalities. In addition, we demonstrate the efficacy of our method on Colored MNIST. |
| Researcher Affiliation | Academia | Itai Gat Technion Idan Schwartz Technion Alexander Schwing UIUC Tamir Hazan Technion |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | Dataset: Colored MNIST [5, 6] is a synthetic dataset based on MNIST [45]. VQA-CPv2 [1] is a re-shuffle of the VQAv2 [46] dataset. The Social IQ dataset is designed to develop models for understanding of social situations in videos. Each sample consists of a video clip, a question, and an answer. The dataset is split into 37,191 training samples, and 5,320 validation set samples. Following the settings of Kim et al. [6], we evaluate our models on the biased Dogs and Cats dataset. |
| Dataset Splits | Yes | The train and validation set consist of 60,000 and 10,000 samples, respectively. VQA-CPv2 consist of 438,183 samples in the train set and 219,928 samples in the test set. The dataset is split into 37,191 training samples, and 5,320 validation set samples. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | Functional Fisher information regularization training on TB1 and testing on TB2 with λ (see Eq. (12)) set to equal 3e-10 results in 94.71% accuracy |