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Found 11 papers, 4 papers with code
Fixed-Weight Difference Target Propagation
Learning of the feedforward and feedback networks is sufficient to make TP methods capable of training, but is having these layer-wise autoencoders a necessary condition for TP to work?
Informative Sample-Aware Proxy for Deep Metric Learning
Among various supervised deep metric learning methods proxy-based approaches have achieved high retrieval accuracies.
PoF: Post-Training of Feature Extractor for Improving Generalization
We developed a training algorithm called PoF: Post-Training of Feature Extractor that updates the feature extractor part of an already-trained deep model to search a flatter minimum.
Feature Space Particle Inference for Neural Network Ensembles
Ensembles of deep neural networks demonstrate improved performance over single models.
Finding a Needle in a Haystack: Tiny Flying Object Detection in 4K Videos using a Joint Detection-and-Tracking Approach
Detecting tiny objects in a high-resolution video is challenging because the visual information is little and unreliable.
Hybrid Loss for Learning Single-Image-based HDR Reconstruction
It makes the reconstruction faithful to the input.
Classification-Reconstruction Learning for Open-Set Recognition
Existing open-set classifiers rely on deep networks trained in a supervised manner on known classes in the training set; this causes specialization of learned representations to known classes and makes it hard to distinguish unknowns from knowns.
Cross-connected Networks for Multi-task Learning of Detection and Segmentation
We evaluated our proposed architecture on a combination of detection and segmentation using two datasets.
Differentiating Objects by Motion: Joint Detection and Tracking of Small Flying Objects
While generic object detection has achieved large improvements with rich feature hierarchies from deep nets, detecting small objects with poor visual cues remains challenging.
Waterdrop Stereo
(2) The imagery inside a water-drop is determined by the water-drop 3D shape and total reflection at the boundary.
Adherent Raindrop Detection and Removal in Video
First, it detects raindrops based on the motion and the intensity temporal derivatives of the input video.
