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SOTA
Sequential Image Classification
Sequential Image Classification On Sequential 1
Sequential Image Classification On Sequential 1
評価指標
Unpermuted Accuracy
評価結果
このベンチマークにおける各モデルのパフォーマンス結果
Columns
モデル名
Unpermuted Accuracy
Paper Title
Repository
S4
91.80%
Efficiently Modeling Long Sequences with Structured State Spaces
SMPConv
84.86%
SMPConv: Self-moving Point Representations for Continuous Convolution
Transformer (self-attention) (Trinh et al., 2018)
62.2%
Learning Longer-term Dependencies in RNNs with Auxiliary Losses
Sparse Combo Net
65.72
RNNs of RNNs: Recursive Construction of Stable Assemblies of Recurrent Neural Networks
MultiresConv
93.15%
Sequence Modeling with Multiresolution Convolutional Memory
LSSL
84.65%
Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers
CKCNN (1M)
63.74%
CKConv: Continuous Kernel Convolution For Sequential Data
LRU
89.0
Resurrecting Recurrent Neural Networks for Long Sequences
LipschitzRNN
64.2
Lipschitz Recurrent Neural Networks
FlexTCN-6
80.82%
FlexConv: Continuous Kernel Convolutions with Differentiable Kernel Sizes
Trellis Network
73.42%
Trellis Networks for Sequence Modeling
UR-GRU
74.4%
Improving the Gating Mechanism of Recurrent Neural Networks
CKCNN (100k)
62.25%
CKConv: Continuous Kernel Convolution For Sequential Data
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