Transductive Decoupled Variational Inference for Few-Shot Classification
Authors: Anuj Rajeeva Singh, Hadi Jamali-Rad
TMLR 2023 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our extensive experimental results corroborate the efficacy of TRIDENT and demonstrate that, using the simplest of backbones and a meta-learning strategy, it sets a new state-of-the-art in the most commonly adopted datasets mini Image Net and tiered Image Net (offering up to 4% and 5% improvements, respectively), as well as for the recent challenging cross-domain mini Imagenet CUB scenario offering a significant margin (up to 20% improvement) beyond the best existing baselines. |
| Researcher Affiliation | Collaboration | 1Delft University of Technology, The Netherlands 2Shell Global Solutions International B.V., Amsterdam, The Netherlands |
| Pseudocode | Yes | Algorithm 1: TRIDENT Algorithm 2: End to End Meta-Training of TRIDENT |
| Open Source Code | Yes | 1Codebase available at https://github.com/anujinho/trident. |
| Open Datasets | Yes | We evaluate TRIDENT on the three most commonly adopted datasets: mini Imagenet (Ravi & Larochelle, 2017a), tiered Imagenet (Ren et al., 2018) and CUB (Welinder et al., 2010). |
| Dataset Splits | Yes | We follow the predominantly adopted settings of Ravi & Larochelle (2017a); Chen et al. (2019) where we split the entire dataset into 64 classes for training, 16 for validation and 20 for testing. tiered Imagenet is a larger subset of Image Net with 608 classes and 779, 165 total images, which are grouped into 34 higher-level nodes in the Image Net human-curated hierarchy. This set of nodes is partitioned into 20, 6, and 8 disjoint sets of training, validation, and testing nodes, and the corresponding classes form the respective meta-sets. CUB (Welinder et al., 2010) dataset has a total of 200 classes, split into training, validation and test sets following Chen et al. (2019). |
| Hardware Specification | Yes | This translates to an average training time of 110 hours on an 11GB NVIDIA 1080Ti GPU. |
| Software Dependencies | No | We use Py Torch (Paszke et al., 2019) and learn2learn (Arnold et al., 2020) for all our implementations. Specific version numbers for these software components are not provided. |
| Experiment Setup | Yes | The hyperparameter values (H.P.) used for training TRIDENT on mini Imagenet and tiered Imagenet are shown in Table 1. We apply the same hyperparameters for the cross-domain testing scenario of mini Imagenet CUB used for training TRIDENT on mini Imagenet, for the given (N-way, K-shot) configuration. Hyperparameters are kept fixed throughout training, validation and testing for a given configuration. Adam (Kingma & Ba, 2015) optimizer is used for inner and meta-updates. |