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Found 10 papers, 1 papers with code
Generalizability of Code Clone Detection on CodeBERT
Transformer networks such as CodeBERT already achieve outstanding results for code clone detection in benchmark datasets, so one could assume that this task has already been solved.
Cross-Domain Evaluation of a Deep Learning-Based Type Inference System
Through our experiments, we detect that the shifts in the dataset and the long-tailed distribution with many rare and unknown data types decrease the performance of the deep learning-based type inference system drastically.
Self-Supervised Learning from Semantically Imprecise Data
Learning from imprecise labels such as "animal" or "bird", but making precise predictions like "snow bunting" at inference time is an important capability for any classifier when expertly labeled training data is scarce.
Making Every Label Count: Handling Semantic Imprecision by Integrating Domain Knowledge
For example, a non-breeding snow bunting is labeled as a bird.
Active and Incremental Learning with Weak Supervision
By selecting unlabeled examples that are promising in terms of model improvement and only asking for respective labels, active learning can increase the efficiency of the labeling process in terms of time and cost.
Not just a matter of semantics: the relationship between visual similarity and semantic similarity
In this paper, we use five different semantic and visual similarity measures each to thoroughly analyze the relationship without relying too much on any single definition.
Integrating domain knowledge: using hierarchies to improve deep classifiers
In this paper, we propose to make use of preexisting class hierarchies like WordNet to integrate additional domain knowledge into classification.
Active Learning for Deep Object Detection
In this paper, we combine a novel method of active learning for object detection with an incremental learning scheme to enable continuous exploration of new unlabeled datasets.
Neither Quick Nor Proper -- Evaluation of QuickProp for Learning Deep Neural Networks
Neural networks and especially convolutional neural networks are of great interest in current computer vision research.
Convolutional Patch Networks with Spatial Prior for Road Detection and Urban Scene Understanding
Classifying single image patches is important in many different applications, such as road detection or scene understanding.
