Conditional Self-Supervised Learning for Few-Shot Classification
Authors: Yuexuan An, Hui Xue, Xingyu Zhao, Lu Zhang
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that our proposed method without any fine-tuning can achieve a significant accuracy improvement on the few-shot classification scenarios compared to the state-of-the-art few-shot learning methods. 4 Experiments In this section, the effectiveness of CSS is verified by various experiments. Three standard few-shot classification datasets (CIFAR-FS [Bertinetto et al., 2019], CUB-200 [Wah et al., 2011], mini-Image Net [Vinyals et al., 2016]) are selected to compare the performance of our approach with previous few-shot learning methods. |
| Researcher Affiliation | Academia | School of Computer Science and Engineering, Southeast University, Nanjing, 210096, China 2MOE Key Laboratory of Computer Network and Information Integration (Southeast University), China {yx an, hxue, xyzhao, lu Zhang}@seu.edu.com |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described, such as a specific repository link or an explicit code release statement. |
| Open Datasets | Yes | Three standard few-shot classification datasets (CIFAR-FS [Bertinetto et al., 2019], CUB-200 [Wah et al., 2011], mini-Image Net [Vinyals et al., 2016]) are selected to compare the performance of our approach with previous fewshot learning methods. |
| Dataset Splits | No | The paper describes meta-training with Dtr and meta-testing with Dte, defining support and query sets within each task. However, it does not provide specific dataset split information (exact percentages, sample counts, or explicit mention of a validation set) needed to reproduce a standard train/validation/test data partitioning. |
| Hardware Specification | Yes | All the computations are performed on a GPU server with NVIDIA TITAN RTX, Intel Core i7-8700 CPU 3.20GHz processor and 32 GB memory. |
| Software Dependencies | No | The paper mentions implementing methods with 'Py Torch' but does not provide specific version numbers for PyTorch or any other ancillary software components. |
| Experiment Setup | Yes | For the fair comparison, we implement all methods by Py Torch. Since [Chen et al., 2019b] shows that the gap among different methods drastically reduces as the backbone gets deeper, all algorithms use the Conv-4-64 [Vinyals et al., 2016] as the backbone and the feature embedding dimension is set to 1600. In CSS, fθ, gξ and hϕ are implement by the Conv-4-64 backbone, and the output dimension of Fω is 2048. The projection MLP head σ is a composite function, which is composed of Fω and a multi-layer neural network with {1600, 2048, 2048} units and batch normalization in each layer. The prediction MLP head δ is parameterized by a three-layer neural network with 512 hidden units and batch normalization in the hidden layer. All hidden layers use ReLU function [Glorot et al., 2011] as the activation function. |