Search Results for author:
Found 8 papers, 6 papers with code
Improve Knowledge Distillation via Label Revision and Data Selection
In addition to the supervision of ground truth, the vanilla KD method regards the predictions of the teacher as soft labels to supervise the training of the student model.
Feature Fusion from Head to Tail for Long-Tailed Visual Recognition
To rectify this issue, we propose to augment tail classes by grafting the diverse semantic information from head classes, referred to as head-to-tail fusion (H2T).
Joint Channel Estimation and Feedback with Masked Token Transformers in Massive MIMO Systems
To achieve joint channel estimation and feedback, this paper proposes an encoder-decoder based network that unveils the intrinsic frequency-domain correlation within the CSI matrix.
Long-tailed Visual Recognition via Gaussian Clouded Logit Adjustment
It is unfavorable for training on balanced data, but can be utilized to adjust the validity of the samples in long-tailed data, thereby solving the distorted embedding space of long-tailed problems.
Ranked #13 on
Long-tail Learning
on CIFAR-10-LT (ρ=100)
Adjusting Logit in Gaussian Form for Long-Tailed Visual Recognition
Based on these perturbed features, two novel logit adjustment methods are proposed to improve model performance at a modest computational overhead.
Feature-Balanced Loss for Long-Tailed Visual Recognition
Deep neural networks frequently suffer from performance degradation when the training data is long-tailed because several majority classes dominate the training, resulting in a biased model.
Ranked #14 on
Long-tail Learning
on CIFAR-10-LT (ρ=100)
Long-Tailed Visual Recognition via Self-Heterogeneous Integration with Knowledge Excavation
To address this problem, state-of-the-art methods usually adopt a mixture of experts (MoE) to focus on different parts of the long-tailed distribution.
FFA-IR: Towards an Explainable and Reliable Medical Report Generation Benchmark
Researchers have explored advanced methods from computer vision and natural language processing to incorporate medical domain knowledge for the generation of readable medical reports.
