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Found 17 papers, 3 papers with code
Action Q-Transformer: Visual Explanation in Deep Reinforcement Learning with Encoder-Decoder Model using Action Query
The decoder in AQT utilizes action queries, which represent the information of each action, as queries.
Learning from AI: An Interactive Learning Method Using a DNN Model Incorporating Expert Knowledge as a Teacher
In this study, based on a deep-learning model that incorporates the knowledge of experts, a method by which a learner "learns from AI" the grounds for its decisions is proposed.
Masking and Mixing Adversarial Training
Adversarial training is a popular and straightforward technique to defend against the threat of adversarial examples.
Data Augmentation by Selecting Mixed Classes Considering Distance Between Classes
Data augmentation is an essential technique for improving recognition accuracy in object recognition using deep learning.
Object-ABN: Learning to Generate Sharp Attention Maps for Action Recognition
Experimental results with UCF101 and SSv2 shows that the generated maps by the proposed method are much clearer qualitatively and quantitatively than those of the original ABN.
ST-ABN: Visual Explanation Taking into Account Spatio-temporal Information for Video Recognition
It is difficult for people to interpret the decision-making in the inference process of deep neural networks.
Deep Ensemble Collaborative Learning by using Knowledge-transfer Graph for Fine-grained Object Classification
In this study, we propose an ensemble method using knowledge transfer to improve the accuracy of ensembles by introducing a loss design that promotes diversity among networks in mutual learning.
Visual Explanation using Attention Mechanism in Actor-Critic-based Deep Reinforcement Learning
A3C consists of a feature extractor that extracts features from an image, a policy branch that outputs the policy, and a value branch that outputs the state value.
Predicting and Attending to Damaging Collisions for Placing Everyday Objects in Photo-Realistic Simulations
Thus, inferring the collision-risk before a placing motion is crucial for achieving the requested task.
Alleviating the Burden of Labeling: Sentence Generation by Attention Branch Encoder-Decoder Network
Domestic service robots (DSRs) are a promising solution to the shortage of home care workers.
Knowledge Transfer Graph for Deep Collaborative Learning
To achieve the knowledge transfer, we propose a novel graph representation called knowledge transfer graph that provides a unified view of the knowledge transfer and has the potential to represent diverse knowledge transfer patterns.
Embedding Human Knowledge into Deep Neural Network via Attention Map
As a result, the fine-tuned network can output an attention map that takes into account human knowledge.
Attention Branch Network: Learning of Attention Mechanism for Visual Explanation
ABN can be applicable to several image recognition tasks by introducing a branch for attention mechanism and is trainable for the visual explanation and image recognition in end-to-end manner.
Survey on Vision-based Path Prediction
Because path prediction as a task of computer vision uses video as input, various information used for prediction, such as the environment surrounding the target and the internal state of the target, need to be estimated from the video in addition to predicting paths.
Denoising random forests
A denoising autoencoder can be trained with indicator vectors produced from clean and noisy input samples, and non-leaf nodes where incorrect decisions are made can be identified by comparing the input and output of the trained denoising autoencoder.
Binary-decomposed DCNN for accelerating computation and compressing model without retraining
Our method replaces real-valued inner-product computations with binary inner-product computations in existing network models to accelerate computation of inference and decrease model size without the need for retraining.
Multiple-Hypothesis Affine Region Estimation With Anisotropic LoG Filters
We propose a method for estimating multiple-hypothesis affine regions from a keypoint by using an anisotropic Laplacian-of-Gaussian (LoG) filter.
