Search Results for author:
Found 5 papers, 1 papers with code
SplitNet: Learnable Clean-Noisy Label Splitting for Learning with Noisy Labels
To address this, some methods were proposed to automatically split clean and noisy labels, and learn a semi-supervised learner in a Learning with Noisy Labels (LNL) framework.
AI-KD: Adversarial learning and Implicit regularization for self-Knowledge Distillation
Our model not only distills the deterministic and progressive knowledge which are from the pre-trained and previous epoch predictive probabilities but also transfers the knowledge of the deterministic predictive distributions using adversarial learning.
ConMatch: Semi-Supervised Learning with Confidence-Guided Consistency Regularization
We present a novel semi-supervised learning framework that intelligently leverages the consistency regularization between the model's predictions from two strongly-augmented views of an image, weighted by a confidence of pseudo-label, dubbed ConMatch.
Semi-Supervised Learning of Semantic Correspondence with Pseudo-Labels
In this paper, we present a simple, but effective solution for semantic correspondence that learns the networks in a semi-supervised manner by supplementing few ground-truth correspondences via utilization of a large amount of confident correspondences as pseudo-labels, called SemiMatch.
AggMatch: Aggregating Pseudo Labels for Semi-Supervised Learning
In this paper, we address this limitation with a novel SSL framework for aggregating pseudo labels, called AggMatch, which refines initial pseudo labels by using different confident instances.
