https://github.com/FightingFighting/GPS
공식 코드Gradient-based Parameter Selection for Efficient Fine-Tuning
Papers with Code 아카이브
Image Classification
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Image Classification is a fundamental task in vision recognition that aims to understand and categorize an image as a whole under a specific label. Unlike object detection, which involves classification and location of multiple objects within an image, image classification typically pertains to single-object images. When the classification becomes highly detailed or reaches instance-level, it is often referred to as image retrieval, which also involves finding similar images in a large database.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/FishYuLi/BalancedGroupSoftmax
공식 코드Overcoming Classifier Imbalance for Long-tail Object Detection with Balanced Group Softmax
https://github.com/ForestAgostinelli/Learned-Activation-Functions-Source
공식 코드Learning Activation Functions to Improve Deep Neural Networks
https://github.com/Forsad/Stable-Polynomial-Train
공식 코드A Training Framework for Optimal and Stable Training of Polynomial Neural Networks
https://github.com/FouriYe/DCRGAN-TMM
공식 코드Disentangling Semantic-to-visual Confusion for Zero-shot Learning
https://github.com/FrankShi9/Attention-Mask-Attack
공식 코드Attention Masks Help Adversarial Attacks to Bypass Safety Detectors
https://github.com/Frankluox/Channel_Importance_FSL
공식 코드https://github.com/Frankluox/FewShotCodeBase
공식 코드Rectifying the Shortcut Learning of Background for Few-Shot Learning
https://github.com/FrostbiteXSW/SCAE_Attack
공식 코드https://github.com/FrostbiteXSW/SCAE_L2_Attack
공식 코드https://github.com/Frozenmad/MetaDelta
공식 코드MetaDelta: A Meta-Learning System for Few-shot Image Classification
https://github.com/GAA-UAM/GB-CNN
공식 코드A Gradient Boosting Approach for Training Convolutional and Deep Neural Networks
https://github.com/GAISSA-UPC/dl-training-energy-estimation
공식 코드https://github.com/GKalliatakis/DisplaceNet
공식 코드DisplaceNet: Recognising Displaced People from Images by Exploiting Dominance Level
https://github.com/GLambard/AdamW_Keras
공식 코드https://github.com/GT-RIPL/DistillMatch-SSCL
공식 코드Memory-Efficient Semi-Supervised Continual Learning: The World is its Own Replay Buffer
https://github.com/GatorSense/DREN
공식 코드Divergence Regulated Encoder Network for Joint Dimensionality Reduction and Classification
https://github.com/GeoX-Lab/ANPyC
공식 코드https://github.com/GeraldHan/GGD
공식 코드https://github.com/GinnyXiao/ReSpike
공식 코드ReSpike: Residual Frames-based Hybrid Spiking Neural Networks for Efficient Action Recognition
https://github.com/GiorgioMorales/HSI-BandSelection
공식 코드Hyperspectral Band Selection for Multispectral Image Classification with Convolutional Networks
https://github.com/Gordonjo/versa
공식 코드https://github.com/GorkaAbad/Sneaky-Spikes
공식 코드https://github.com/GorkemP/labeled-images-for-ulcerative-colitis
공식 코드Class Distance Weighted Cross-Entropy Loss for Ulcerative Colitis Severity Estimation
https://github.com/GroupOfAlchemists/SeqConv
공식 코드https://github.com/HAL-42/FMA-WSSS
공식 코드Foundation Model Assisted Weakly Supervised Semantic Segmentation
https://github.com/HCPLab-SYSU/HSE
공식 코드Fine-Grained Representation Learning and Recognition by Exploiting Hierarchical Semantic Embedding
https://github.com/HCPLab-SYSU/SSGRL
공식 코드Learning Semantic-Specific Graph Representation for Multi-Label Image Recognition
https://github.com/HHU-MMBS/plp-official-tmlr2024
공식 코드Adapting Contrastive Language-Image Pretrained (CLIP) Models for Out-of-Distribution Detection
https://github.com/HIK-LAB/Unsupervised-Image-Classification
공식 코드Unsupervised Image Classification for Deep Representation Learning
https://github.com/HPAI-BSC/MAMe-baselines
공식 코드The MAMe Dataset: On the relevance of High Resolution and Variable Shape image properties
https://github.com/HPAI-BSC/MetH-baselines
공식 코드MetH: A family of high-resolution and variable-shape image challenges
https://github.com/HRNet/HRFormer
공식 코드https://github.com/HaimFisher/fingerprints-armor
공식 코드https://github.com/Hananel-Hazan/bindsnet
공식 코드https://github.com/HanxunH/Active-Passive-Losses
공식 코드Normalized Loss Functions for Deep Learning with Noisy Labels
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.