Label Efficient Learning of Transferable Representations acrosss Domains and Tasks
Authors: Zelun Luo, Yuliang Zou, Judy Hoffman, Li F. Fei-Fei
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our method shows compelling results on novel classes within a new domain even when only a few labeled examples per class are available, outperforming the prevalent fine-tuning approach. In addition, we demonstrate the effectiveness of our framework on the transfer learning task from image object recognition to video action recognition. |
| Researcher Affiliation | Academia | Zelun Luo Stanford University zelunluo@stanford.edu Yuliang Zou Virginia Tech ylzou@vt.edu Judy Hoffman University of California, Berkeley jhoffman@eecs.berkeley.edu Li Fei-Fei Stanford University feifeili@cs.stanford.edu |
| Pseudocode | No | The paper contains mathematical equations but no structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about open-sourcing the code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | We present results transferring from a subset of Google Street View House Numbers (SVHN) [41] containing only digits 0-4 to a subset of MNIST [29] containing only digits 5-9. Secondly, we present results on the challenging setting of adapting from Image Net [6] object-centric images to UCF-101 [57] videos for action recognition. |
| Dataset Splits | Yes | We subsample k examples from each class to construct dataset D2 so that we can perform traditional training or episodic (k 1)-shot learning. We experiment with k = 2, 3, 4, 5...we randomly subsample 10 different subsets {Di 2}10 i=1 from the training split of MNIST dataset, and use the remaining data as {Di 3}10 i=1 for each k. |
| Hardware Specification | No | The paper does not specify any particular hardware (e.g., GPU models, CPU types) used for the experiments, only mentioning the use of the PyTorch framework. |
| Software Dependencies | No | The paper mentions that 'We conduct all the experiments with the Py Torch framework' but does not specify a version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | We use the temperature τ = 2 for source-target semantic transfer and τ = 1 for within target transfer... We use α = 0.1 and β = 0.1 in our objective function. The network is trained with Adam optimizer [25] and with learning rate 10 3. |