Towards Reusable Network Components by Learning Compatible Representations
Authors: Michael Gygli, Jasper Uijlings, Vittorio Ferrari7620-7629
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We systematically analyse these approaches on the task of image classification on standard datasets. We demonstrate that we can produce components which are directly compatible without any fine-tuning or compromising accuracy on the original tasks. Afterwards, we demonstrate the use of compatible components on three applications: Unsupervised domain adaptation, transferring classifiers across feature extractors with different architectures, and increasing the computational efficiency of transfer learning. |
| Researcher Affiliation | Industry | Michael Gygli, Jasper Uijlings, Vittorio Ferrari Google Research gyglim@google.com, jrru@google.com, vittoferrari@google.com |
| Pseudocode | No | The paper describes its methods and training schemes in prose and mathematical formulations but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or providing links to a code repository for the methodology described. |
| Open Datasets | Yes | We systematically analyse these approaches on the task of image classification on standard datasets. ... We train one network on the CIFAR-10 (Krizhevsky 2009) train set and one on the STL10 (Coates, Ng, and Lee 2011) train set. ... It is common to start from a model pre-trained for ILSVRC-12 classification (Donahue et al. 2013; Ren et al. 2015; He et al. 2017). |
| Dataset Splits | No | The paper mentions training on 'CIFAR-10 train set' and 'STL10 train set', and evaluating on 'CIFAR-10 test' and 'STL-10 test'. While these are standard datasets with defined train/test splits, the paper does not explicitly provide details about a separate validation split, its size, or how it was used. |
| Hardware Specification | No | The paper does not provide 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 dependencies with version numbers, such as programming language versions or library versions (e.g., 'Python 3.8, PyTorch 1.9'). |
| Experiment Setup | No | While the paper discusses network architectures (ResNet-56, WRN-28, MobileNet V2), and mentions 'fine-tuning for 1000 steps' or 'up to 5 epochs' in specific contexts, it lacks comprehensive details on the experimental setup such as specific learning rates, batch sizes, optimizer choices, or full training schedules for the main experiments. |