https://github.com/mpagli/agree-to-disagree
공식 코드Agree to Disagree: Diversity through Disagreement for Better Transferability
Papers with Code 아카이브
Image Classification
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- 33
- 평가표
- 177
- 보존된 평가 기록
- 2,906
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/mpatacchiola/contextual-squeeze-and-excitation
공식 코드Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification
https://github.com/mpatacchiola/imujoco
공식 코드Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy Imitation
https://github.com/mqalkhatib/Real_Complex_Classification
공식 코드Attention based Dual-Branch Complex Feature Fusion Network for Hyperspectral Image Classification
https://github.com/mqalkhatib/sdf2net
공식 코드SDF2Net: Shallow to Deep Feature Fusion Network for PolSAR Image Classification
https://github.com/mr7495/image-classification-spatial
공식 코드https://github.com/mrT23/TResNet
공식 코드https://github.com/mrchenfeng/clipcleaner_acmmm2024
공식 코드https://github.com/mrflogs/LoRA-Pro
공식 코드https://github.com/mriffiaslett/ips_maxriffiaslett
공식 코드https://github.com/mrkshllr/FewTURE
공식 코드https://github.com/mrkshllr/bixt
공식 코드Perceiving Longer Sequences With Bi-Directional Cross-Attention Transformers
https://github.com/msahamed/earthquake_physics_bayesian_nn
공식 코드Estimating uncertainty of earthquake rupture using Bayesian neural network
https://github.com/mschuessler/two4two
공식 코드Two4Two: Evaluating Interpretable Machine Learning - A Synthetic Dataset For Controlled Experiments
https://github.com/msgun/weightedsiamese
공식 코드https://github.com/msgun/xbl-d
공식 코드https://github.com/msu-mlsys-lab/deepaa
공식 코드https://github.com/mtayyab454/CNN-compression-ICIP2021
공식 코드Compressing Deep CNNs using Basis Representation and Spectral Fine-tuning
https://github.com/mtsang/interaction_interpretability
공식 코드https://github.com/mu-cai/frl
공식 코드Out-of-distribution Detection via Frequency-regularized Generative Models
https://github.com/muberraozmen/mrmp
공식 코드Multi-relation Message Passing for Multi-label Text Classification
https://github.com/mueedhafiz1982/CNNTreeEnsemble
공식 코드Deep Network Ensemble Learning applied to Image Classification using CNN Trees
https://github.com/mug-auth/ssl-chewing
공식 코드https://github.com/muradtuk/SVMCoreset
공식 코드https://github.com/murphyzc/metakan
공식 코드Improving Memory Efficiency for Training KANs via Meta Learning
https://github.com/murufeng/EPSANet
공식 코드EPSANet: An Efficient Pyramid Squeeze Attention Block on Convolutional Neural Network
https://github.com/musab-r/TCN
공식 코드Teacher-Class Network: A Neural Network Compression Mechanism
https://github.com/mustansarfiaz/scratchformer
공식 코드Remote Sensing Change Detection With Transformers Trained from Scratch
https://github.com/mv-lab/ViT-FGVC8
공식 코드Exploring Vision Transformers for Fine-grained Classification
https://github.com/mvp18/ttt-ucdr
공식 코드https://github.com/mvrl/birdsat
공식 코드BirdSAT: Cross-View Contrastive Masked Autoencoders for Bird Species Classification and Mapping
https://github.com/mvrl/vlpl
공식 코드Vision-Language Pseudo-Labels for Single-Positive Multi-Label Learning
https://github.com/mwolff31/signal_preference
공식 코드Signal Strength and Noise Drive Feature Preference in CNN Image Classifiers
https://github.com/mx54039q/entroformer
공식 코드Entroformer: A Transformer-based Entropy Model for Learned Image Compression
https://github.com/mxbonn/ltmp
공식 코드Learned Thresholds Token Merging and Pruning for Vision Transformers
https://github.com/myfavouritekk/vdetlib
공식 코드Object Detection from Video Tubelets with Convolutional Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.