argmax centroid
Authors: Chengyue Gong, Mao Ye, Qiang Liu
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the applicability and effectiveness of our method on a variety of real-world multitask learning applications, including few-shot image classification, personalized dialogue systems and multi-target domain adaptation. |
| Researcher Affiliation | Academia | Chengyue Gong Mao Ye Qiang Liu Computer Science Department, The University of Texas at Austin {cygong17,my21,lqiang}@cs.utexas.edu |
| Pseudocode | Yes | Algorithm 1 Main Algorithm: Argmax Centroids for Approximating |
| Open Source Code | No | The paper does not provide any explicit statement about open-sourcing code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | Standard benchmarks of few-shot classification are chosen for experiments. We evaluate all the baselines and our algorithms on two subsets of Image Net, Mini-Image Net and Tiered Image Net (Sun et al., 2019). |
| Dataset Splits | Yes | Mini-Image Net contains 64 classes for training, 16 for validation and 20 for test. |
| Hardware Specification | No | The paper does not explicitly describe any specific hardware components (e.g., GPU/CPU models, memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper mentions software components like 'BERT-base' and 'Adam' optimizer but does not provide specific version numbers for these or any other software dependencies needed for replication. |
| Experiment Setup | Yes | In all experiments, we set the replacement controller = 1.2 and = 0.5 for Algorithm 1. In the experiments, for few-shot learning based on SIB and IFSL, we set n = 16. For meta training, we use Adam (Kingma & Ba, 2014) with learning rate 10 3, 10 2 for inner and outer loop training, respectively. During the evaluation, for all the models, we used beam search with beam size 4 and length penalty 1.2. |