  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
assigning query samples to the nearest class prototype, and (2) a pairwise
Laplacian term encouraging nearby query samples to have consistent label
assignments. Our transductive inference does not re-train the base model, and
can be viewed as a graph clustering of the query set, subject to supervision
constraints from the support set. We derive a computationally efficient bound
optimizer of a relaxation of our function, which computes independent
(parallel) updates for each query sample, while guaranteeing convergence.
Following a simple cross-entropy training on the base classes, and without
complex meta-learning strategies, we conducted 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 transductive inference is very fast,
with computational times that are close to inductive inference, and can be used
for large-scale few-shot tasks.
