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Found 9 papers, 0 papers with code
Noise2Image: Noise-Enabled Static Scene Recovery for Event Cameras
Event cameras capture changes of intensity over time as a stream of 'events' and generally cannot measure intensity itself; hence, they are only used for imaging dynamic scenes.
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.
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.
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.
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.
