Laplacian Regularized Few-Shot Learning
We propose a transductive Laplacian-regularized inference for few-shot tasks. Given any feature embedding learned from the base classes, we minimize a quadratic binary-assignment function containing two terms: (1) a unary term assign- ing query samples to the nearest class prototype, and (2) a pairwise Laplacian term encouraging nearby query samples to have consistent label as- signments. Our transductive inference does not re-train the base model, and can be viewed as a graph clustering of the query set, subject to super- vision constraints from the support set. We derive a computationally efficient bound optimizer of a relaxation of our function, which computes inde- pendent (parallel) updates for each query sample, while guaranteeing convergence. Following a sim- ple cross-entropy training on the base classes, and without complex meta-learning strategies, we con- ducted comprehensive experiments over five few- shot learning benchmarks. Our LaplacianShot consistently outperforms state-of-the-art methods by significant margins across different models, settings, and data sets. Furthermore, our trans- ductive inference is very fast, with computational times that are close to inductive inference, and can be used for large-scale few-shot tasks.
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Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
---|---|---|---|---|---|---|
Few-Shot Image Classification | CUB 200 5-way 1-shot | LaplacianShot | Accuracy | 80.96 | # 16 | |
Few-Shot Image Classification | CUB 200 5-way 5-shot | LaplacianShot | Accuracy | 88.68 | # 20 | |
Few-Shot Image Classification | iNaturalist (227-way multi-shot) | LaplacianShot | Accuracy | 74.97 | # 1 | |
Few-Shot Image Classification | Mini-Imagenet 5-way (1-shot) | LaplacianShot | Accuracy | 75.57 | # 20 | |
Few-Shot Image Classification | Mini-Imagenet 5-way (5-shot) | LaplacianShot | Accuracy | 84.72 | # 22 | |
Few-Shot Image Classification | miniImagenet β CUB (5-way 1-shot) | LaplacianShot | Accuracy | 55.46 | # 1 | |
Few-Shot Image Classification | miniImagenet β CUB (5-way 5-shot) | LaplacianShot | Accuracy | 66.33 | # 1 | |
Few-Shot Image Classification | Mini-ImageNet-CUB 5-way (5-shot) | LaplacianShot | Accuracy | 66.33 | # 5 | |
Few-Shot Image Classification | Tiered ImageNet 5-way (1-shot) | LaplacianShot | Accuracy | 80.30 | # 11 | |
Few-Shot Image Classification | Tiered ImageNet 5-way (5-shot) | LaplacianShot | Accuracy | 87.93 | # 16 |