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Found 8 papers, 0 papers with code
Dynamic Structured Illumination Microscopy with a Neural Space-time Model
We propose a new method, Speckle Flow SIM, that uses static patterned illumination with moving samples and models the sample motion during data capture in order to reconstruct the dynamic scene with super-resolution.
Prostate cancer inference via weakly-supervised learning using a large collection of negative MRI
Here, we propose the baseline MRI model to alternatively learn the appearance of mp-MRI using radiology-confirmed negative MRI cases via weakly supervised learning.
Mining Interpretable AOG Representations from Convolutional Networks via Active Question Answering
The AOG associates each object part with certain neural units in feature maps of conv-layers.
Explanatory Graphs for CNNs
This paper introduces a graphical model, namely an explanatory graph, which reveals the knowledge hierarchy hidden inside conv-layers of a pre-trained CNN.
Interactively Transferring CNN Patterns for Part Localization
In the scenario of one/multi-shot learning, conventional end-to-end learning strategies without sufficient supervision are usually not powerful enough to learn correct patterns from noisy signals.
Interpreting CNN Knowledge via an Explanatory Graph
Considering that each filter in a conv-layer of a pre-trained CNN usually represents a mixture of object parts, we propose a simple yet efficient method to automatically disentangles different part patterns from each filter, and construct an explanatory graph.
Mining Object Parts from CNNs via Active Question-Answering
We use an active human-computer communication to incrementally grow such an AOG on the pre-trained CNN as follows.
Growing Interpretable Part Graphs on ConvNets via Multi-Shot Learning
This paper proposes a learning strategy that extracts object-part concepts from a pre-trained convolutional neural network (CNN), in an attempt to 1) explore explicit semantics hidden in CNN units and 2) gradually grow a semantically interpretable graphical model on the pre-trained CNN for hierarchical object understanding.
