ALIFE: Adaptive Logit Regularizer and Feature Replay for Incremental Semantic Segmentation
Authors: Youngmin Oh, Donghyeon Baek, Bumsub Ham
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of our approach with extensive experiments on standard ISS benchmarks, and show that our method achieves a better trade-off in terms of accuracy and efficiency. |
| Researcher Affiliation | Academia | Youngmin Oh Donghyeon Baek Bumsub Ham School of Electrical and Electronic Engineering, Yonsei University |
| Pseudocode | Yes | We provide the pseudo code in the supplementary material. |
| Open Source Code | Yes | https://cvlab.yonsei.ac.kr/projects/ALIFE. ...Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] |
| Open Datasets | Yes | We evaluate our method on standard ISS benchmarks (PASCAL VOC [10] and ADE20K [40]). PASCAL VOC provides 10, 582 training [13] and 1, 449 validation samples with 20 object and one background categories, while ADE20K consists of 20, 210 and 2, 000 samples for training and validation, respectively, with 150 object/stuff categories. |
| Dataset Splits | Yes | PASCAL VOC provides 10, 582 training [13] and 1, 449 validation samples... while ADE20K consists of 20, 210 and 2, 000 samples for training and validation, respectively... For evaluation, we report Io U scores on the validation set for each dataset |
| Hardware Specification | Yes | We use 2 NVIDIA TITAN RTX GPUs for all experiments. Please refer to the supplementary material for details. |
| Software Dependencies | No | The paper mentions software components like Deep Lab-V3, Res Net-101, SGD optimizer, and Adam optimizer but does not specify their version numbers or the versions of underlying frameworks (e.g., PyTorch, TensorFlow). |
| Experiment Setup | Yes | We use the SGD optimizer with an initial learning rate set to 1e-2 and 1e-3 for base and incremental stages, respectively. ... Deep Lab-V3 is trained for 30 and 60 epochs at a base stage (t = 1) on PASCAL VOC [10] and ADE20K [40], respectively. ... We train rotation matrices for 10 epochs using the Adam optimizer with an initial learning rate of 1e-3, and fix a preset number S and a temperature value τ to 1, 000 and 10 for all experiments. We fine-tune a classifier for 1 epoch using the SGD optimizer with an initial learning rate of 1e-3. For all experiments, we adjust the learning rate by the poly schedule. |