Deep Subspace Clustering Networks
Authors: Pan Ji, Tong Zhang, Hongdong Li, Mathieu Salzmann, Ian Reid
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show that our method significantly outperforms the state-of-the-art unsupervised subspace clustering techniques. |
| Researcher Affiliation | Academia | Pan Ji University of Adelaide Tong Zhang Australian National University Hongdong Li Australian National University Mathieu Salzmann EPFL CVLab Ian Reid University of Adelaide |
| Pseudocode | No | The paper describes the network architecture and training strategy in text and figures, but does not provide a formal pseudocode block or algorithm. |
| Open Source Code | Yes | Due to the lack of space, we refer the reader to the publicly available implementation of SSC and Section 5 of [15], as well as to the Tensor Flow implementation of our algorithm 2 for more detail. (Footnote 2: https://github.com/panji1990/Deep-subspace-clustering-networks) |
| Open Datasets | Yes | We extensively evaluate our method on face clustering, using the Extended Yale B [21] and ORL [39] datasets, and on general object clustering, using COIL20 [31] and COIL100 [30]. |
| Dataset Splits | No | The paper describes pre-training and fine-tuning using all available data (e.g., 'we build a big batch using all the data to minimize the loss L(Θ)') but does not explicitly mention or specify a separate validation set for hyperparameter tuning or early stopping. |
| Hardware Specification | No | The paper does not specify any particular hardware components (e.g., CPU, GPU models, memory, or cloud instance types) used for running the experiments. |
| Software Dependencies | Yes | We implemented our method in Python with Tensorflow-1.0 [1], |
| Experiment Setup | Yes | Specifically, we use Adam [18], an adaptive momentum based gradient descent method, to minimize the loss, where we set the learning rate to 1.0 10 3 in all our experiments. We set the regularization parameters to λ1 = 1.0, λ2 = 1.0 10 K 10 3. In the fine-tuning stage, we ran 30 epochs (COIL20) / 100 epochs (COIL100) for DSC-Net-L1 and 30 epochs (COIL20) / 120 epochs (COIL100) for DSC-Net-L2, and set the regularization parameters to λ1 = 1, λ2 = 150/30 (COIL20/COIL100). |