Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation
Authors: Seunghoon Hong, Hyeonwoo Noh, Bohyung Han
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that our network substantially outperforms existing semi-supervised techniques based on DNNs even with much smaller segmentation annotations, e.g., 5 or 10 strong annotations per class. Section 6 presents experimental results on a challenging benchmark dataset. |
| Researcher Affiliation | Academia | Dept. of Computer Science and Engineering, POSTECH, Pohang, Korea |
| Pseudocode | No | The paper describes the proposed methods using text and diagrams, but it does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states 'Refer to our project website4 for more comprehensive qualitative evaluation' (Footnote 4: 'http://cvlab.postech.ac.kr/research/decouplednet/'). This is a project website for evaluation, not an explicit statement that the source code for the methodology is available, nor does it provide a specific repository link. |
| Open Datasets | Yes | We employ PASCAL VOC 2012 dataset [1] for training and testing of the proposed deep network. The dataset with extended annotations from [16], which contains 12,031 images with pixel-wise class labels, is used in our experiment. There are 10,582 images with image-level class labels, which are used to train our classification network. |
| Dataset Splits | Yes | We evaluate the performance of the proposed algorithm on 1,449 validation images. We also construct training datasets with strongly annotated images; the number of images with segmentation labels per class is controlled to evaluate the impact of supervision level. |
| Hardware Specification | Yes | training takes 3 days (0.5 day for classification network and 2.5 days for segmentation network) in a single Nvidia GTX Titan X GPU with 12G memory. |
| Software Dependencies | No | The paper states 'The proposed network is implemented based on Caffe library [17]' but does not provide a specific version number for Caffe or any other software dependency. |
| Experiment Setup | Yes | The standard Stochastic Gradient Descent (SGD) with momentum is employed for optimization, where all parameters are identical to [12]. We use mini-batches of 64 examples in training classification and segmentation networks, respectively; training takes 3 days (0.5 day for classification network and 2.5 days for segmentation network). We perform combinatorial cropping proposed in Section 5 for the images with strong annotations, where Edge Box [15] is adopted to generate region proposals and the Np(= 200) sub-images are generated for each label combination. |