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Self supervised learning

Mix-and-Match Tuning for Self-Supervised Semantic Segmentation

Self supervised learning

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Mix-and-Match Tuning for Self-
Supervised Semantic Segmentation
AAAI
2018
Self-taught learning
Supervised learning
Semi-supervised learning
Domain adaptation
Self-taught learning
Proxy task
(a) Sparse coding
(b) Colorization (c) Context
Drawbacks
Take learning by colorization as an
example, the goal of the proxy task is
to colorize gray-scale images. The
representations learned from
colorization may be well suited for
modeling color distributions, but are
likely amateur in discriminating high-
level semantics. For instance, as
shown in Fig. 1(a), a red car can be
arbitrarily more similar to a red bus
than to a blue car. The features of
both car and bus classes are highly
overlapped, as depicted by the
feature embedding in the left plot of
Fig. 1(b). 000000
Framework
Classic Self-taught learning:
(a) -> (c)
Proposed method:
(a) -> (b) -> (c)
Mix & Match
Mix step: We randomly sample a large number of local
patches from the target set and mix them together. Heavily
overlapped patches are discarded. These patches are
represented by using the features extracted from the CNN
pre-trained in the stage of Fig. 2(a), and assigned with
unique class labels based on the corresponding label map.
Match step: For each CNN learning iteration, we construct a
graph on-the-fly given a batch of input images. The nodes of
the graph are patches. Two types of edges are defined
between nodes – a) “attractive” if two nodes have an
identical class label and b) “rejective” if two nodes have
different class labels.

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Self supervised learning

  • 1. Mix-and-Match Tuning for Self- Supervised Semantic Segmentation AAAI 2018
  • 2. Self-taught learning Supervised learning Semi-supervised learning Domain adaptation Self-taught learning
  • 3. Proxy task (a) Sparse coding (b) Colorization (c) Context
  • 4. Drawbacks Take learning by colorization as an example, the goal of the proxy task is to colorize gray-scale images. The representations learned from colorization may be well suited for modeling color distributions, but are likely amateur in discriminating high- level semantics. For instance, as shown in Fig. 1(a), a red car can be arbitrarily more similar to a red bus than to a blue car. The features of both car and bus classes are highly overlapped, as depicted by the feature embedding in the left plot of Fig. 1(b). 000000
  • 5. Framework Classic Self-taught learning: (a) -> (c) Proposed method: (a) -> (b) -> (c)
  • 6. Mix & Match Mix step: We randomly sample a large number of local patches from the target set and mix them together. Heavily overlapped patches are discarded. These patches are represented by using the features extracted from the CNN pre-trained in the stage of Fig. 2(a), and assigned with unique class labels based on the corresponding label map. Match step: For each CNN learning iteration, we construct a graph on-the-fly given a batch of input images. The nodes of the graph are patches. Two types of edges are defined between nodes – a) “attractive” if two nodes have an identical class label and b) “rejective” if two nodes have different class labels.
  • 9. Result in term of mean IoU
  • 10. Result in term of per-class IoU