Learning Debiased and Disentangled Representations for Semantic Segmentation
Authors: Sanghyeok Chu, Dongwan Kim, Bohyung Han
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Models trained with our approach demonstrate strong results on multiple semantic segmentation benchmarks, with especially notable performance gains on under-represented classes. To evaluate the effectiveness of Drop Class, we experiment on a well-known semantic segmentation dataset: Cityscapes [6] with a few reasons. |
| Researcher Affiliation | Collaboration | Sanghyeok Chu Dongwan Kim Bohyung Han ECE & ASRI, Seoul National University {sanghyeok.chu,dongwan123,bhhan}@snu.ac.kr. This work was partly supported by Samsung Advanced Institute of Technology, Korean ICT R&D programs of the MSIT/IITP grant [2017-0-01779, XAI, 2021-0-01343, Artificial Intelligence Graduate School Program (Seoul National University)], and the Bio & Medical Technology Development Program of the National Research Foundation (NRF) funded by the Korea government (MSIT) [2021M3A9E4080782]. |
| Pseudocode | Yes | Algorithm 1 Training scheme for Drop Class. |
| Open Source Code | No | The paper provides links to third-party network architectures (HRNet, PyTorch DeepLabV3) that were used, but does not provide a link to the authors' own implementation of the proposed 'Drop Class' methodology. |
| Open Datasets | Yes | To evaluate the effectiveness of Drop Class, we experiment on a well-known semantic segmentation dataset: Cityscapes [6] with a few reasons. First, it is relatively small, with 2975 train images. Second, it has large class imbalances, with the pixel frequency ranging from 0.1% to 36.9%. These two commonalities make it difficult for an ordinary model to learn robust, debiased representations. We also conduct experiments on the Pascal VOC dataset [14], and the results can be found in the supplementary document. |
| Dataset Splits | Yes | Cityscapes [6] with a few reasons. First, it is relatively small, with 2975 train images. To achieve this, we design an unbiased test set (I*), where the validation set is copied 19 times, and one of the 19 classes in Cityscapes is erased from each copy. |
| Hardware Specification | Yes | The rest of our hyperparameters are organized as a table in the supplementary materials, where we detail the number of iterations, batch size, learning rate, learning rate decay, image size, and type of GPU used for each of our experiments. |
| Software Dependencies | No | The paper mentions using Py Torch [16] but does not provide a specific version number for it or other software dependencies. |
| Experiment Setup | Yes | We use the same set of hyperparameters for both experiments to ensure fair comparison. The value of the loss weighing term α in Eq. (9) is set to 10 for all experiments, which is based on the scale of the two loss terms. As mentioned in Section 2.3, the value of λ is initialized as 0 and scaled linearly up to 1 across the duration of training. To further stabilize training, we linearly increase the probability of dropping out any class from 0 to 1 as well. |