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Found 11 papers, 4 papers with code
Replacement Learning: Training Vision Tasks with Fewer Learnable Parameters
To address these issues, we propose an innovative training approach called Replacement Learning, which mitigates these limitations by completely replacing all the parameters of the frozen layers with only two learnable parameters.
GSO-YOLO: Global Stability Optimization YOLO for Construction Site Detection
Despite achieving impressive performance, advanced object detection methods like YOLOv8 still face challenges in handling the complex conditions found at construction sites.
MLAAN: Scaling Supervised Local Learning with Multilaminar Leap Augmented Auxiliary Network
Specifically, MLAAN comprises Multilaminar Local Modules (MLM) and Leap Augmented Modules (LAM).
Class Information Guided Reconstruction for Automatic Modulation Open-Set Recognition
To enhance distinguishability, we design Class Conditional Vectors (CCVs) to match the latent representations extracted from input samples, achieving perfect reconstruction for known samples while yielding poor results for unknown ones.
SAM: A Self-adaptive Attention Module for Context-Aware Recommendation System
In this work, we propose a novel and general self-adaptive module, the Self-adaptive Attention Module (SAM), which adjusts the selection bias by capturing contextual information based on its representation.
Leveraged Weighted Loss for Partial Label Learning
As an important branch of weakly supervised learning, partial label learning deals with data where each instance is assigned with a set of candidate labels, whereas only one of them is true.
Two-stage Training for Learning from Label Proportions
Learning from label proportions (LLP) aims at learning an instance-level classifier with label proportions in grouped training data.
OT-LLP: Optimal Transport for Learning from Label Proportions
Learning from label proportions (LLP), where the training data are arranged in form of groups with only label proportions provided instead of the exact labels, is an important weakly supervised learning paradigm in machine learning.
LOSSLESS SINGLE IMAGE SUPER RESOLUTION FROM LOW-QUALITY JPG IMAGES
Super Resolution (SR) is a fundamental and important low-level computer vision (CV) task.
s-LWSR: Super Lightweight Super-Resolution Network
Deep learning (DL) architectures for superresolution (SR) normally contain tremendous parameters, which has been regarded as the crucial advantage for obtaining satisfying performance.
Learning from Label Proportions with Generative Adversarial Networks
In this paper, we leverage generative adversarial networks (GANs) to derive an effective algorithm LLP-GAN for learning from label proportions (LLP), where only the bag-level proportional information in labels is available.
