Dynamic Filter Networks
Authors: Xu Jia, Bert De Brabandere, Tinne Tuytelaars, Luc V. Gool
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of the dynamic filter network on the tasks of video and stereo prediction, and reach state-of-the-art performance on the moving MNIST dataset with a much smaller model. and 4 Experiments The Dynamic Filter Network can be used in different ways in a wide variety of applications. In this section we show its application in learning steerable filters, video prediction and stereo prediction. All code to reproduce the experiments is available at https://github.com/ dbbert/dfn. |
| Researcher Affiliation | Academia | Bert De Brabandere1 ESAT-PSI, KU Leuven, i Minds Xu Jia1 ESAT-PSI, KU Leuven, i Minds Tinne Tuytelaars1 ESAT-PSI, KU Leuven, i Minds Luc Van Gool1,2 ESAT-PSI, KU Leuven, i Minds D-ITET, ETH Zurich 1firstname.lastname@esat.kuleuven.be 2vangool@vision.ee.ethz.ch |
| Pseudocode | No | The paper does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | All code to reproduce the experiments is available at https://github.com/ dbbert/dfn. |
| Open Datasets | Yes | Moving MNIST We first evaluate the method on the synthetic moving MNIST dataset [19]. Given a sequence of 10 frames with two moving digits as input, the goal is to predict the following 10 frames. We use the code provided by [19] to generate training samples on-the-fly, and use the provided test set for comparison. |
| Dataset Splits | No | The paper mentions training and test sets but does not explicitly describe a separate validation set or a three-way split for any of the datasets used. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependency details with version numbers, such as programming languages, libraries, or frameworks used for implementation. |
| Experiment Setup | No | The paper mentions the loss functions used (binary cross-entropy, Euclidean loss) and the filter size (9x9), but defers comprehensive details on hyperparameters (e.g., learning rate, batch size, epochs) to the external code: 'Details on the hyper-parameter can be found in the available code.' |