Compacting, Picking and Growing for Unforgetting Continual Learning

Continual lifelong learning is essential to many applications. In this paper, we propose a simple but effective approach to continual deep learning... (read more)

PDF Abstract NeurIPS 2019 PDF NeurIPS 2019 Abstract

Results from the Paper


 Ranked #1 on Age And Gender Classification on Adience Gender (using extra training data)

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TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK USES EXTRA
TRAINING DATA
RESULT BENCHMARK
Age And Gender Classification Adience Age CPG (single crop, pytorch) Accuracy (5-fold) 57.66 # 1
Age And Gender Classification Adience Gender CPG (single crop, pytorch) Accuracy (5-fold) 89.66 # 1
Facial Expression Recognition AffectNet CPG Accuracy 63.57 # 2
Continual Learning Cifar100 (20 tasks) CPG Average Accuracy 80.9 # 1
Continual Learning CUBS (Fine-grained 6 Tasks) CPG Accuracy 83.59 # 2
Continual Learning Flowers (Fine-grained 6 Tasks) CPG Accuracy 96.62 # 1
Continual Learning ImageNet (Fine-grained 6 Tasks) CPG Accuracy 75.81 # 2
Face Verification Labeled Faces in the Wild CPG Accuracy 99.30% # 14
Continual Learning Sketch (Fine-grained 6 Tasks) CPG Accuracy 80.33 # 1
Continual Learning Stanford Cars (Fine-grained 6 Tasks) CPG Accuracy 92.80 # 1
Continual Learning Wikiart (Fine-grained 6 Tasks) CPG Accuracy 77.15 # 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