https://github.com/swalpa/LiSHT
공식 코드LiSHT: Non-Parametric Linearly Scaled Hyperbolic Tangent Activation Function for Neural Networks
Papers with Code 아카이브
Image Classification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 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/swapnasamirshukla/BT5153-Term-Project
공식 코드Neural Networks for Fashion Image Classification and Visual Search
https://github.com/swufung/ADMMSoftmax
공식 코드ADMM-SOFTMAX : An ADMM Approach for Multinomial Logistic Regression
https://github.com/syliz517/clip-reid
공식 코드CLIP-ReID: Exploiting Vision-Language Model for Image Re-Identification without Concrete Text Labels
https://github.com/symplecticgeometry/equivariant-neural-networks-and-equivarification
공식 코드https://github.com/synxlin/deep-gradient-compression
공식 코드Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training
https://github.com/szagoruyko/diracnets
공식 코드DiracNets: Training Very Deep Neural Networks Without Skip-Connections
https://github.com/szagoruyko/wide-residual-networks
공식 코드https://github.com/szbonaldo/fedmlp
공식 코드FedMLP: Federated Multi-Label Medical Image Classification under Task Heterogeneity
https://github.com/szc12153/sparse_meta_tuning
공식 코드Unleashing the Power of Meta-tuning for Few-shot Generalization Through Sparse Interpolated Experts
https://github.com/szc19990412/TransMIL
공식 코드https://github.com/szegedai/robust-segmentation-evaluation
공식 코드Evaluating the Adversarial Robustness of Semantic Segmentation: Trying Harder Pays Off
https://github.com/szhang42/Contextual_dropout_release
공식 코드Contextual Dropout: An Efficient Sample-Dependent Dropout Module
https://github.com/szq0214/MEAL-V2
공식 코드MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks
https://github.com/szq0214/fkd
공식 코드A Fast Knowledge Distillation Framework for Visual Recognition
https://github.com/szq0214/sret
공식 코드https://github.com/szx503045266/asf-former
공식 코드https://github.com/taegyeong-lee/makd
공식 코드Multi-aspect Knowledge Distillation with Large Language Model
https://github.com/taehokim20/cprune
공식 코드CPrune: Compiler-Informed Model Pruning for Efficient Target-Aware DNN Execution
https://github.com/taghikhah/quantod
공식 코드Quantile-based Maximum Likelihood Training for Outlier Detection
https://github.com/tahabelkhouja/srs
공식 코드Out-of-Distribution Detection in Time-Series Domain: A Novel Seasonal Ratio Scoring Approach
https://github.com/tahmid0007/Low_Pass_ReLU
공식 코드Robust Image Classification Using A Low-Pass Activation Function and DCT Augmentation
https://github.com/tajanthan/pmf
공식 코드https://github.com/tajwarfahim/dcm
공식 코드Conservative Prediction via Data-Driven Confidence Minimization
https://github.com/tajwarfahim/ood_detection_inconsistency
공식 코드No True State-of-the-Art? OOD Detection Methods are Inconsistent across Datasets
https://github.com/takashiishida/comp
공식 코드Complementary-Label Learning for Arbitrary Losses and Models
https://github.com/takedarts/swgridnet
공식 코드SwGridNet: A Deep Convolutional Neural Network based on Grid Topology for Image Classification
https://github.com/takerum/vat_chainer
공식 코드Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning
https://github.com/takerum/vat_tf
공식 코드Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning
https://github.com/talalwasim/textguidedresilience
공식 코드Hardware Resilience Properties of Text-Guided Image Classifiers
https://github.com/tamaramueller/deuterium_partial_sensitivity
공식 코드https://github.com/tanganke/fusion_bench
공식 코드https://github.com/tangminji/noisywikihow
공식 코드NoisywikiHow: A Benchmark for Learning with Real-world Noisy Labels in Natural Language Processing
https://github.com/tankche1/IDeMe-Net
공식 코드https://github.com/tanmay-ty/SpectralNET
공식 코드SpectralNET: Exploring Spatial-Spectral WaveletCNN for Hyperspectral Image Classification
https://github.com/tanmdl/watt-effnet
공식 코드WATT-EffNet: A Lightweight and Accurate Model for Classifying Aerial Disaster Images
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.