Charting the Right Manifold: Manifold Mixup for Few-shot Learning

Code

• github.com/nupurkmr9/S2M2_fewshot
  OfficialIn paperpytorch★ 0
• github.com/ShuoYang-1998/Few_Shot_Distribution_Calibration
  pytorch★ 472
• github.com/ShuoYang-1998/ICLR2021-Oral_Distribution_Calibration
  pytorch★ 472
• github.com/yhu01/PT-MAP
  pytorch★ 215
• github.com/danielshalam/bpa
  pytorch★ 63
• github.com/allenhaozhu/ease
  pytorch★ 10
• github.com/breakaway7/p3dc-shot
  pytorch★ 1
• github.com/DanielShalam/SOT
  pytorch★ 0

Abstract

Few-shot learning algorithms aim to learn model parameters capable of adapting to unseen classes with the help of only a few labeled examples. A recent regularization technique - Manifold Mixup focuses on learning a general-purpose representation, robust to small changes in the data distribution. Since the goal of few-shot learning is closely linked to robust representation learning, we study Manifold Mixup in this problem setting. Self-supervised learning is another technique that learns semantically meaningful features, using only the inherent structure of the data. This work investigates the role of learning relevant feature manifold for few-shot tasks using self-supervision and regularization techniques. We observe that regularizing the feature manifold, enriched via self-supervised techniques, with Manifold Mixup significantly improves few-shot learning performance. We show that our proposed method S2M2 beats the current state-of-the-art accuracy on standard few-shot learning datasets like CIFAR-FS, CUB, mini-ImageNet and tiered-ImageNet by 3-8 %. Through extensive experimentation, we show that the features learned using our approach generalize to complex few-shot evaluation tasks, cross-domain scenarios and are robust against slight changes to data distribution.

Tasks

Benchmark Results

Reproductions