Generalizable Features From Unsupervised Learning
Authors: Mehdi Mirza, Aaron Courville, Yoshua Bengio
ICLR 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate that an unsupervised model, trained to predict future frames of a video sequence of stable and unstable block configurations, can yield features that support extrapolating stability prediction to blocks configurations outside the training set distribution. Figure 4 shows the classification results for each of the 9 models described in Section 4.2 tested on 3, 4 and 5 blocks. |
| Researcher Affiliation | Academia | Mehdi Mirza & Aaron Courville & Yoshua Bengio MILA Universit e de Montr eal {memirzamo, aaron.courville, yoshua.umontreal}@gmail.com |
| Pseudocode | No | No structured pseudocode or algorithm blocks are present in the paper. Tables 1 and 2 describe model architectures but are not pseudocode. |
| Open Source Code | No | The paper does not provide an explicit statement or a link to open-source code for the methodology described. Footnote 1 links to an external physics engine, not the authors' implementation. |
| Open Datasets | No | We, therefore, construct a new dataset, with a similar setup as Lerer et al. (2016); Zhang et al. (2016), that includes this video sequence. We use a Javascript based physics engine1 to generate the data. The paper describes generating its own dataset but does not provide access information or state its public availability. |
| Dataset Splits | Yes | For each tower height, we create 8000, 1000 and 3000 video clips for the training, validation, and test set, respectively. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running experiments. |
| Software Dependencies | No | The paper mentions specific algorithms and architectures like 'Ada M Optimizer' and 'Res Net', and a 'Javascript based physics engine', but does not provide version numbers for any software dependencies. |
| Experiment Setup | Yes | All activation functions are Re LU(Nair & Hinton, 2010). The objective function is the mean squared error between the generated last frame and the ground-truth frame; as a result, this training will not require any labels. Mean squared error is minimized using the Ada M Optimizer(Kingma & Ba, 2014) and we use earlystopping when the validation loss does not improve for 100 epochs. For training, we further subsample in time dimension and reduce the sequence length to 5-time steps. All images are contrast normalized independently and we augment our training set using random horizontal flip of the images and randomly changing the contrast and brightness. |