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Found 6 papers, 0 papers with code
Unsupervised Finetuning
This problem is more challenging than the supervised counterpart, as the low data density in the small-scale target data is not friendly for unsupervised learning, leading to the damage of the pretrained representation and poor representation in the target domain.
Improve Unsupervised Pretraining for Few-label Transfer
Unsupervised pretraining has achieved great success and many recent works have shown unsupervised pretraining can achieve comparable or even slightly better transfer performance than supervised pretraining on downstream target datasets.
Are Fewer Labels Possible for Few-shot Learning?
We conduct experiments on 10 different few-shot target datasets, and our average few-shot performance outperforms both vanilla inductive unsupervised transfer and supervised transfer by a large margin.
Density-Aware Graph for Deep Semi-Supervised Visual Recognition
Motivated by these limitations, this paper proposes to solve the SSL problem by building a novel density-aware graph, based on which the neighborhood information can be easily leveraged and the feature learning and label propagation can also be trained in an end-to-end way.
Memory-Based Neighbourhood Embedding for Visual Recognition
Learning discriminative image feature embeddings is of great importance to visual recognition.
3D-DETNet: a Single Stage Video-Based Vehicle Detector
Video-based vehicle detection has received considerable attention over the last ten years and there are many deep learning based detection methods which can be applied to it.
Ranked #9 on
Object Detection
on UA-DETRAC
