614 citations · 1.5k across the 27 of their papers we have counts for
12 papers · 1 filter
Class-Incremental Learning by Knowledge Distillation with Adaptive Feature Consolidation
Minsoo Kang, Jaeyoo Park, Bohyung Han
We present a novel class incremental learning approach based on deep neural networks, which continually learns new tasks with limited memory for storing examples in the previous ta…
Information-Theoretic Bias Reduction via Causal View of Spurious Correlation
Seonguk Seo, Joon-Young Lee, Bohyung Han
We propose an information-theoretic bias measurement technique through a causal interpretation of spurious correlation, which is effective to identify the feature-level algorithmic…
Operation-Aware Soft Channel Pruning using Differentiable Masks
Minsoo Kang, Bohyung Han
We propose a simple but effective data-driven channel pruning algorithm, which compresses deep neural networks in a differentiable way by exploiting the characteristics of operatio…
Towards Oracle Knowledge Distillation with Neural Architecture Search
Minsoo Kang, Jonghwan Mun, Bohyung Han
We present a novel framework of knowledge distillation that is capable of learning powerful and efficient student models from ensemble teacher networks. Our approach addresses the…
Efficient Decoupled Neural Architecture Search by Structure and Operation Sampling
Heung-Chang Lee, Do-Guk Kim, Bohyung Han
We propose a novel neural architecture search algorithm via reinforcement learning by decoupling structure and operation search processes. Our approach samples candidate models fro…
Regularizing Neural Networks via Stochastic Branch Layers
Wonpyo Park, Paul Hongsuck Seo, Bohyung Han +1
We introduce a novel stochastic regularization technique for deep neural networks, which decomposes a layer into multiple branches with different parameters and merges stochastical…