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Understanding Individual Decisions of CNNs via Contrastive Backpropagation
Jindong Gu, Yinchong Yang, Volker Tresp
A number of backpropagation-based approaches such as DeConvNets, vanilla Gradient Visualization and Guided Backpropagation have been proposed to better understand individual decisi…
Efficient Dialog Policy Learning via Positive Memory Retention
Rui Zhao, Volker Tresp
This paper is concerned with the training of recurrent neural networks as goal-oriented dialog agents using reinforcement learning. Training such agents with policy gradients typic…
Energy-Based Hindsight Experience Prioritization
Rui Zhao, Volker Tresp
In Hindsight Experience Replay (HER), a reinforcement learning agent is trained by treating whatever it has achieved as virtual goals. However, in previous work, the experience was…
Improving Visual Relationship Detection using Semantic Modeling of Scene Descriptions
Stephan Baier, Yunpu Ma, Volker Tresp
Structured scene descriptions of images are useful for the automatic processing and querying of large image databases. We show how the combination of a semantic and a visual statis…
Improving Information Extraction from Images with Learned Semantic Models
Stephan Baier, Yunpu Ma, Volker Tresp
Many applications require an understanding of an image that goes beyond the simple detection and classification of its objects. In particular, a great deal of semantic information…
Learning Goal-Oriented Visual Dialog via Tempered Policy Gradient
Rui Zhao, Volker Tresp
Learning goal-oriented dialogues by means of deep reinforcement learning has recently become a popular research topic. However, commonly used policy-based dialogue agents often end…