CVPR-2019 Unsupervised Embedding Learning via Invariant and Spreading Instance Feature

· CVPR· · unsupervised-learning

Paper: Unsupervised Embedding Learning via Invariant and Spreading Instance Feature

InvaSpread: Instance-discrimination surrogate task + NCE loss + end-to-end learning

  1. Positive and negative pairs are drawn from a minibatch processed by the same encoder. Without TPU-scale batching, training stays end-to-end and needs no external data structures, which improves speed; in practice, performance improves further when more negatives are available.
  2. The paper explains the mathematical rationale behind the instance-wise objective—why it encourages spread-out and invariant representations.
  3. It stresses that learning deeper feature embeddings yields better generalization.

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