Understanding Distributed Representations of Concepts in Deep Neural Networks without Supervision
Authors: Wonjoon Chang, Dahee Kwon, Jaesik Choi
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we present the qualitative and quantitative evaluation results of our proposed method as well as various use cases. Our experiments are conducted on the Mini Image Net (Vinyals et al. 2016), Flowers Recognition (denoted by Flowers), Oxford pet, Broden (Bau et al. 2017), Imagenet-X (Idrissi et al. 2022) datasets, using VGG19 (Simonyan and Zisserman 2014), Res Net50 (He et al. 2016), and Mobile Net V2 (Sandler et al. 2018) models. |
| Researcher Affiliation | Collaboration | Wonjoon Chang1 *, Dahee Kwon1 *, Jaesik Choi1, 2 1 Korea Advanced Institute of Science and Technology 2 INEEJI |
| Pseudocode | Yes | Algorithm 1: Finding a Relaxed Decision Region |
| Open Source Code | No | The paper does not provide any information about open-source code availability or links to code repositories. |
| Open Datasets | Yes | Our experiments are conducted on the Mini Image Net (Vinyals et al. 2016), Flowers Recognition (denoted by Flowers), Oxford pet, Broden (Bau et al. 2017), Imagenet-X (Idrissi et al. 2022) datasets |
| Dataset Splits | No | The paper mentions "training data" and refers to "validation" in the quantitative evaluation but does not provide specific percentages, counts, or explicit methodologies for dataset splits for training, validation, or testing. It mentions: "We empirically check that the RDR works effectively with the parameters k [5, 10] and t [9, 15] in the penultimate convolutional block of the models in our experiments." which is a range and not a concrete value. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers that are critical dependencies for reproducibility. |
| Experiment Setup | No | The paper states, "Detailed settings of each experiment are provided in Appendix." and mentions |