Random Path Selection for Continual Learning
Authors: Jathushan Rajasegaran, Munawar Hayat, Salman H. Khan, Fahad Shahbaz Khan, Ling Shao
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through extensive experiments, we demonstrate that the proposed method surpasses the state-of-the-art performance on incremental learning and by utilizing parallel computation this method can run in constant time with nearly the same efficiency as a conventional deep convolutional neural network. Also, Section 4 is titled "Experiments and Results" and contains detailed experimental evaluation including comparisons, ablation studies, and performance metrics. |
| Researcher Affiliation | Industry | Inception Institute of Artificial Intelligence first.last@inceptioniai.org |
| Pseudocode | No | The paper does not contain any structured pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | Codes available at https://github.com/brjathu/RPSnet |
| Open Datasets | Yes | For our experiments, we use evaluation protocols similar to i CARL [21]. We incrementally learn 100 classes on CIFAR-100 in groups of 10, 20 and 50 at a time. For Image Net, we use the same subset as [21] comprising of 100 classes and incrementally learn them in groups of 10. ... We also experiment our model with MNIST and SVHN datasets. |
| Dataset Splits | No | The paper mentions using 'evaluation protocols similar to i CARL [21]' and evaluating on 'test samples of all seen classes', but it does not explicitly state specific percentages or counts for a distinct validation dataset split. The information provided is insufficient to confirm a validation split. |
| Hardware Specification | Yes | For each task, we train N = 8 models in parallel using a NVIDIA-DGX-1 machine. |
| Software Dependencies | No | The paper mentions using 'Adam [14]' for optimization but does not provide specific version numbers for any software libraries or dependencies, only the general optimizer name. |
| Experiment Setup | Yes | For each task, we train our model for 100 epochs using Adam [14] with te = 2, with learning rate starting from 10 3 and divided by 2 after every 20 epochs. We set the controller s scaling factor to γ = 2.5 and γ = 10 respectively for CIFAR and Image Net datasets. We fix M = 8 and J = 2 except for the 50 classes per task, where J = 1. We do not use any weight or network regularization scheme such as dropout in our model. For augmentation, training images are randomly cropped, flipped and rotated (< 100). |