Data Fine-Tuning
Authors: Saheb Chhabra, Puspita Majumdar, Mayank Vatsa, Richa Singh8223-8230
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments performed on three publicly available datasets LFW, Celeb A, and MUCT, demonstrate the effectiveness of the proposed concept. |
| Researcher Affiliation | Academia | IIIT-Delhi, India {sahebc, pushpitam, mayank, rsingh}@iiitd.ac.in |
| Pseudocode | No | No explicit pseudocode or algorithm block was found. Figure 3 is a "Block diagram illustrating the steps of the proposed algorithm", but not a pseudocode listing. |
| Open Source Code | No | No explicit statement or link providing access to the source code for the described methodology was found. |
| Open Datasets | Yes | Experiments are performed on three publicly available datasets and results showcase enhanced performance of black box systems using data fine-tuning. |
| Dataset Splits | Yes | The dataset is partitioned into 60% training set, 20% validation set, and 20% testing set. (LFW, MUCT)... 162,770 images in the training set, 19,867 into validation set, and 19,962 images in the testing set. (Celeb A) |
| Hardware Specification | No | No specific hardware (e.g., GPU model, CPU type) used for running the experiments was explicitly mentioned. |
| Software Dependencies | No | The paper mentions "Adam optimizer" and "VGGFace + NNET" but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | Perturbation Learning: To learn the perturbation for a given dataset, learning rate is set to 0.001 and the batch size is 800. The number of iterations used for processing each batch is 16, and the number of epochs is 5. Model Fine-tuning: To fine-tune the attribute classification model, Adam optimizer is used with learning rate set to 0.005. The model is trained for 20 epochs. |