A Baseline for Few-Shot Image Classification
Authors: Guneet Singh Dhillon, Pratik Chaudhari, Avinash Ravichandran, Stefano Soatto
ICLR 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show results of transductive fine-tuning on benchmark datasets in few-shot learning, namely Mini-Image Net (Vinyals et al., 2016), Tiered-Image Net (Ren et al., 2018), CIFAR-FS (Bertinetto et al., 2018) and FC-100 (Oreshkin et al., 2018), in Section 4.1. |
| Researcher Affiliation | Collaboration | Guneet S. Dhillon1, Pratik Chaudhari2 , Avinash Ravichandran1, Stefano Soatto1,3 1Amazon Web Services, 2University of Pennsylvania, 3University of California, Los Angeles |
| Pseudocode | No | The paper describes methods through text and mathematical equations, but does not include any formally labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link indicating that the source code for their described methodology is open-source. |
| Open Datasets | Yes | The Mini-Image Net dataset (Vinyals et al., 2016)...we obtained the dataset from the authors of Gidaris & Komodakis (2018)3 https://github.com/gidariss/Few Shot Without Forgetting |
| Dataset Splits | Yes | Each dataset has a training, validation and test set consisting of disjoint sets of classes. |
| Hardware Specification | Yes | We use data parallelism across 8 Nvidia V100 GPUs and half-precision training using techniques from Micikevicius et al. (2017); Howard et al. (2018). |
| Software Dependencies | No | The paper mentions various software components and techniques (e.g., SGD, Adam, batch-normalization), but does not provide specific version numbers for the software dependencies needed for replication. |
| Experiment Setup | Yes | We pre-train using standard data augmentation, cross-entropy loss with label smoothing (Szegedy et al., 2016) of ϵ=0.1, mixup regularization (Zhang et al., 2017) of α=0.25, SGD with batch-size of 256, Nesterov s momentum of 0.9, weight-decay of 10 4 and no dropout. |