/hedged_instance_embedding

embedding method for measuring uncertainty

Primary LanguagePython

hedged_instance_embedding

embedding method for measuring uncertainty

Introduction(from abstract)

Often the distance between points is used as a proxy for match confidence. However, this can fail to represent uncertainty arising when the input is ambiguous, e.g., due to occlusion or blurriness

This work addresses this issue and explicitly models the uncertainty by hedging the location of each input in the embedding space.

Dependency

  • numpy 1.16.3
  • tensorflow 2.0
  • tfp-nightly 0.7.0
  • matplotlib 3.0.3
  • sklearn 0.21.1

Usage

  • train
python train.py [option]
  • visualize
python visualize.py [option]

Method

soft constrative loss: point embedding loss

In paper, see 2 - 1 - (3)

VIB loss: hedged instance embedding loss

In paper, see 2 - 3 - (8)

Result: Point Emb VS HIB Emb

  • Input image 80: clean vs occlusion

80c 800

  • Input image 58: clean vs occlusion

58c 58o

Reference