Selective Task Group Updates for Multi-Task Optimization
Authors: Wooseong Jeong, Kuk-Jin Yoon
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental settings. We assess the proposed techniques using three datasets: NYUD-V2 for indoor vision tasks (Silberman et al., 2012), PASCAL-Context for outdoor scenarios (Mottaghi et al., 2014), and Taskonomy (Zamir et al., 2018) for large number of tasks. Multi-task performance is compared using the metric introduced by (Maninis et al., 2019). This metric calculates the per-task performance by averaging it relative to the single-task baseline b: m = (1/K) PK i=1( 1)li(Mm,i Mb,i)/Mb,i where li = 1 if a lower value of measure Mi means indicates better performance for task τi, and 0 otherwise. More details are introduced in Appendix C. |
| Researcher Affiliation | Academia | Wooseong Jeong & Kuk-Jin Yoon Korea Advanced Institute of Science and Technology EMAIL |
| Pseudocode | Yes | Algorithm 1: Tracking Proximal Inter-Task Affinity for Task Group Updates |
| Open Source Code | No | We implement our experiments on top of publically available code from Ye & Xu (2022b). We run our experiments on A6000 GPUs. |
| Open Datasets | Yes | We assess the proposed techniques using three datasets: NYUD-V2 for indoor vision tasks (Silberman et al., 2012), PASCAL-Context for outdoor scenarios (Mottaghi et al., 2014), and Taskonomy (Zamir et al., 2018) for large number of tasks. |
| Dataset Splits | No | We assess the proposed techniques using three datasets: NYUD-V2 for indoor vision tasks (Silberman et al., 2012), PASCAL-Context for outdoor scenarios (Mottaghi et al., 2014), and Taskonomy (Zamir et al., 2018) for large number of tasks. Explanation: The paper mentions using well-known datasets but does not explicitly provide details about the training, validation, or test splits (e.g., percentages or sample counts) used for these datasets in the provided text. |
| Hardware Specification | Yes | We run our experiments on A6000 GPUs. |
| Software Dependencies | No | Table C.1: Hyperparameters for experiments. Hyperparameter Value Optimizer Adam Kingma & Ba (2014) Scheduler Polynomial Decay Minibatch size 8 Number of iterations 40000 Backbone (Transformer) Vi T Dosovitskiy et al. (2020) Learning rate 0.00002 Weight Decay 0.000001 Affinity decay factor β 0.001. Explanation: The paper lists software components like "Adam" and "Vi T" but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | Implementation Details. For experiments, we adopt Vi T Dosovitskiy et al. (2020) pre-trained on Image Net-22K Deng et al. (2009) as the multi-task encoder. Task-specific decoders merge the multi-scale features extracted by the encoder to generate the outputs for each task. The models are trained for 40,000 iterations on both NYUD Silberman et al. (2012) and PASCAL Everingham & Winn (2012) datasets with batch size 8. We used Adam optimizer with learning rate 2 10 5 and 1 10 6 of a weight decay with a polynomial learning rate schedule. The cross-entropy loss was used for semantic segmentation, human parts estimation, and saliency, edge detection. Surface normal prediction and depth estimation used L1 loss. The tasks are weighted equally to ensure a fair comparison. For the Taskonomy Benchmark Zamir et al. (2018), we use the dataloader from the open-access code provided by Chen et al. (2023), while maintaining experimental settings identical to those used for NYUD-v2 and PASCAL-Context. We use the same experimental setup for the other hyperparameters as in previous works Ye & Xu (2022a;c), as detailed in Table C.1. Table C.1: Hyperparameters for experiments. Hyperparameter Value Optimizer Adam Kingma & Ba (2014) Scheduler Polynomial Decay Minibatch size 8 Number of iterations 40000 Backbone (Transformer) Vi T Dosovitskiy et al. (2020) Learning rate 0.00002 Weight Decay 0.000001 Affinity decay factor β 0.001 |