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Hard Attention
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/baidu-research/DT-RAM
https://github.com/chilung/hat
Overcoming catastrophic forgetting with hard attention to the task
https://github.com/conan7882/recurrent-attention-model
https://github.com/dmlc/dgl/tree/master/examples/pytorch/hardgat
Graph Representation Learning via Hard and Channel-Wise Attention Networks
https://github.com/ibm/graph_ensemble_learning
https://github.com/johnrobinsn/catch
https://github.com/keonlee9420/Stepwise_Monotonic_Multihead_Attention
Robust Sequence-to-Sequence Acoustic Modeling with Stepwise Monotonic Attention for Neural TTS
https://github.com/kevinzakka/recurrent-visual-attention
https://github.com/kiwi1944/CRISense
https://github.com/lienchibao1998/new
Learning Visual Question Answering by Bootstrapping Hard Attention
https://github.com/luxxxlucy/recurrent-attention-model-revisited
https://github.com/mengdi-li/robotic-occlusion-reasoning
https://github.com/mimikaan/Attention-Model
https://github.com/parsatorb/PyTorch-Saccader
Saccader: Improving Accuracy of Hard Attention Models for Vision
https://github.com/qingzew/tensorflow-ram
https://github.com/rajeev595/RHS_HierNSE
Read, Highlight and Summarize: A Hierarchical Neural Semantic Encoder-based Approach
https://github.com/stephanboner/gabor_data
https://github.com/theowu23451/DRAM
https://github.com/tianyu-tristan/Visual-Attention-Model
https://github.com/ufal/acl2019_nested_ner
https://github.com/ulstu/ml
https://github.com/ulstu/robotics_ml
https://github.com/xuefusiji/badrefsr
Learning Texture Transformer Network for Image Super-Resolution
https://gitlab.com/exwzd-public/kiritani_ono_2020
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.