Learned versus Hand-Designed Feature Representations for 3d Agglomeration
Authors: John A. Bogovic; Gary B. Huang; Viren Jain
ICLR 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate a large set of hand-designed 3d feature descriptors alongside features learned from the raw data using both end-to-end and unsupervised learning techniques, in the context of agglomeration of 3d neuron fragments. By combining unsupervised learning techniques with a novel dynamic pooling scheme, we show how pure learning-based methods are for the first time competitive with hand-designed 3d shape descriptors. We investigate data augmentation strategies for dramatically increasing the size of the training set, and show how combining both learned and hand-designed features leads to the highest accuracy. |
| Researcher Affiliation | Academia | John A. Bogovic, Gary B. Huang & Viren Jain Janelia Farm Research Campus Howard Hughes Medical Institute 19700 Helix Drive, Ashburn, VA, USA {bogovicj, huangg, jainv}@janelia.hhmi.org |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | No | The paper describes using custom data: 'Tissue from a drosophila melanogaster brain was imaged using focused ion-beam scanning electron microscopy (FIB-SEM [21]) at a resolution of 8 8 8 nm. ... We supplied the DAWMR network with 120 megavoxels of hand-segmented image data for training'. No concrete access information for this dataset is provided. |
| Dataset Splits | No | The paper describes training and test sets: 'One of the two volumes was randomly chosen to be the training set (14, 522 edges: 7968 positive and 6584 negative), and the other volume serves as a test set (14, 829 edges: 8342 positive and 6487 negative)'. A separate validation set is not explicitly described with specific details. |
| Hardware Specification | No | The paper mentions the hardware used for image acquisition ('focused ion-beam scanning electron microscopy (FIB-SEM)'), but it does not specify any computational hardware (e.g., GPUs, CPUs, servers) used for running the experiments. |
| Software Dependencies | No | The paper mentions software components like 'dropout multilayer perceptron (MLP) [16]', 'decision-stump boosting classifier [13]', and 'DAWMR network [17]', citing the relevant papers. However, it does not provide specific version numbers for these software dependencies (e.g., Python 3.x, TensorFlow 2.x, PyTorch 1.x). |
| Experiment Setup | Yes | As our classifier, we use a drop-out multilayer perceptron (200 hidden units, 500, 000 weight updates, rectified linear hidden units) [16], but also present results using a decision-stump boosting classifier [13]. |