Hard Attention
35 papers with code • 0 benchmarks • 0 datasets
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Use these libraries to find Hard Attention models and implementationsMost implemented papers
Recurrent Models of Visual Attention
kevinzakka/recurrent-visual-attention
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NeurIPS 2014
Applying convolutional neural networks to large images is computationally expensive because the amount of computation scales linearly with the number of image pixels.
Deep Attention Recurrent Q-Network
A deep learning approach to reinforcement learning led to a general learner able to train on visual input to play a variety of arcade games at the human and superhuman levels.
Dual Attention Networks for Few-Shot Fine-Grained Recognition
msfuxian/DualAttentionNet
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Proceedings of the AAAI Conference on Artificial Intelligence 2022
Specifically, by producing attention guidance from deep activations of input images, our hard-attention is realized by keeping a few useful deep descriptors and forming them as a bag of multi-instance learning.
Overcoming catastrophic forgetting with hard attention to the task
joansj/hat
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ICML 2018
In this paper, we propose a task-based hard attention mechanism that preserves previous tasks' information without affecting the current task's learning.
Hard Non-Monotonic Attention for Character-Level Transduction
shijie-wu/neural-transducer
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EMNLP 2018
We compare soft and hard non-monotonic attention experimentally and find that the exact algorithm significantly improves performance over the stochastic approximation and outperforms soft attention.
Exact Hard Monotonic Attention for Character-Level Transduction
shijie-wu/neural-transducer
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ACL 2019
Our models achieve state-of-the-art performance on morphological inflection.
Saccader: Improving Accuracy of Hard Attention Models for Vision
google-research/google-research
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NeurIPS 2019
Although deep convolutional neural networks achieve state-of-the-art performance across nearly all image classification tasks, their decisions are difficult to interpret.
Progressive Attention Networks for Visual Attribute Prediction
We propose a novel attention model that can accurately attends to target objects of various scales and shapes in images.
Morphological Inflection Generation with Hard Monotonic Attention
We present a neural model for morphological inflection generation which employs a hard attention mechanism, inspired by the nearly-monotonic alignment commonly found between the characters in a word and the characters in its inflection.
Reinforced Self-Attention Network: a Hybrid of Hard and Soft Attention for Sequence Modeling
taoshen58/DiSAN
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In this paper, we integrate both soft and hard attention into one context fusion model, "reinforced self-attention (ReSA)", for the mutual benefit of each other.
