Memory Efficient Meta-Learning with Large Images
Authors: John Bronskill, Daniela Massiceti, Massimiliano Patacchiola, Katja Hofmann, Sebastian Nowozin, Richard Turner
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we demonstrate that meta-learners trained with LITE achieve state-of-the-art performance among meta-learners on two challenging few-shot classification benchmarks: (i) ORBIT [14] which is a real-world few-shot object recognition dataset for teachable object recognizers; and (ii) VTAB+MD [11] which is composed of the Visual Task Adaptation Benchmark (VTAB) [20] and Meta-Dataset (MD) [13] and combines both few-shot and transfer learning tasks. |
| Researcher Affiliation | Collaboration | John Bronskill University of Cambridge jfb54@cam.ac.uk Daniela Massiceti Microsoft Research dmassiceti@microsoft.com Massimiliano Patacchiola University of Cambridge mp2008@cam.ac.uk Katja Hofmann Microsoft Research kahofman@microsoft.com Sebastian Nowozin Microsoft Research senowoz@microsoft.com Richard E. Turner University of Cambridge ret26@cam.ac.uk |
| Pseudocode | Yes | Algorithm 1 LITE for a meta-training task τ |
| Open Source Code | Yes | 2Source code for ORBIT experiments is available at https://github.com/microsoft/ORBIT-Dataset and for the VTAB+MD experiments at https://github.com/cambridge-mlg/LITE. |
| Open Datasets | Yes | ORBIT [14] which is a real-world few-shot object recognition dataset for teachable object recognizers; and (ii) VTAB+MD [11] which is composed of the Visual Task Adaptation Benchmark (VTAB) [20] and Meta-Dataset (MD) [13] |
| Dataset Splits | Yes | The benchmark splits data collectors into disjoint train, validation, and test user sets along with their corresponding objects and videos. |
| Hardware Specification | Yes | Simple CNAPS + LITE trains in about 20 hours on a single 16GB GPU. |
| Software Dependencies | No | The paper mentions "PyTorch" and "TensorFlow Datasets" but does not provide specific version numbers for these or any other software dependencies required to replicate the experiments. |
| Experiment Setup | Yes | Experiments We meta-train Proto Nets [3], CNAPS [4] and Simple CNAPs [5] with LITE on tasks composed of large (224 224) images... For each model, we consider a Res Net-18 (RN-18) and Efficient Net-B0 (EN-B0) feature extractor, both pre-trained on Image Net [29]. We follow the task sampling protocols described in [14] (see Appendices B and C.1 for details). |