AutoSlim: Towards One-Shot Architecture Search for Channel Numbers

218 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Dynamic Slimmable Network (2021, unknown): “2.9B 75.1 ThiNet-70 [53, 52] 2.9B 75.8 MetaPruning 0.85 [51] 3.0B 76.2 ConvNet-AIG-50 [63] 3.1B 76.2 AutoSlim [71] 3.0B 76.0 DS-ResNet-L (Ours) 3.1B 76.6”
BossNAS: Exploring Hybrid CNN-transformers with Block-wisely Self-supervised Neural Architecture Search (2021, background): “Can-didates of different channel numbers in our supernet share the weights in a slimmable manner [78, 77, 76, 38, 9].”
Universal ASR: Unify and Improve Streaming ASR with Full-context Modeling (2020, methodology): “Moreover, motivated by Inplace Knowledge Distillation (Yu & Huang, 2019b) in which a large model is used to supervise a small model, we propose to distill knowledge from the full-context mode (teacher) into the streaming mode (student) on the ﬂy within the same Universal ASR model, by encouraging co”
AOWS: Adaptive and Optimal Network Width Search With Latency Constraints (2020, methodology): “In section 3, we further detail these prior works [32, 33, 31], highlighting their similarities and differences with our approach, which we introduce in sections 4 to 6.”
CARS: Continuous Evolution for Efficient Neural Architecture Search (2019, methodology): “The efficient block design [54, 53, 52], tensor decomposition [24, 49, 50, 42, 43], pruning [25, 26, 12], distillation [4, 48] and quantization [22] are popular techniques to make networks efficient.”