Beyond Unimodal: Generalising Neural Processes for Multimodal Uncertainty Estimation
Authors: Myong Chol Jung, He Zhao, Joanna Dipnall, Lan Du
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In extensive empirical evaluation, our method achieves state-of-the-art multimodal uncertainty estimation performance, showing its appealing robustness against noisy samples and reliability in out-of-distribution detection with faster computation time compared to the current state-of-the-art multimodal uncertainty estimation method. We conduct rigorous experiments on seven real-world datasets and achieve the new SOTA performance in classification accuracy, calibration, robustness to noise, and OOD detection. We evaluated the proposed method on the seven real-world datasets and compared its performance against seven unimodal and multimodal baselines. |
| Researcher Affiliation | Academia | Myong Chol Jung Monash University david.jung@monash.edu; He Zhao CSIRO s Data61 he.zhao@ieee.org; Joanna Dipnall Monash University jo.dipnall@monash.edu; Lan Du Monash University lan.du@monash.edu |
| Pseudocode | Yes | Figure 2d: Pseudocode of MNPs |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the described methodology. |
| Open Datasets | Yes | In this experiment, we evaluated the classification performance of MNPs and the robustness to noisy samples with six multimodal datasets [24, 17]. These datasets lie within a feature space where each feature extraction method can be found in [17]. Table 6: Multimodal datasets used for evaluating robustness to noisy samples. We used CIFAR10-C [18] which consists of corrupted images of CIFAR10 [31]. |
| Dataset Splits | No | Following [24] and [17], we normalised the datasets and used a train-test split of 0.8:0.2. This specifies a train-test split but does not explicitly define a separate validation split. |
| Hardware Specification | Yes | All the experiments were conducted on a single NVIDIA Ge Force RTX 3090 GPU. |
| Software Dependencies | No | For all the experiments, we used the Adam optimiser [30] with batch size of 200 and the Tensorflow framework. While TensorFlow is mentioned, no specific version number for the framework or other software dependencies are provided. |
| Experiment Setup | Yes | For all the experiments, we used the Adam optimiser [30] with batch size of 200 and the Tensorflow framework. Table 7: Hyperparameters of MNPs. |