Learning Discrete Representations via Information Maximizing Self-Augmented Training

ICML 2017 Weihua HuTakeru MiyatoSeiya TokuiEiichi MatsumotoMasashi Sugiyama

Learning discrete representations of data is a central machine learning task because of the compactness of the representations and ease of interpretation. The task includes clustering and hash learning as special cases... (read more)

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Results from the Paper


Ranked #2 on Unsupervised Image Classification on SVHN (using extra training data)

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TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK USES EXTRA
TRAINING DATA
RESULT BENCHMARK
Unsupervised Image Classification SVHN IMSAT Acc 57.30 # 2
# of clusters (k) 10 # 1

Methods used in the Paper


METHOD TYPE
🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet