https://github.com/creinders/chimeramix
공식 코드ChimeraMix: Image Classification on Small Datasets via Masked Feature Mixing
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/cristianopatricio/explainable-deep-learning-methods-in-medical-image-classification-a-survey
공식 코드Explainable Deep Learning Methods in Medical Image Classification: A Survey
https://github.com/cristianopatricio/unsupervised-contrastive-cond-diff
공식 코드Unsupervised Contrastive Analysis for Salient Pattern Detection using Conditional Diffusion Models
https://github.com/cryptonymous9/mixing-ssl
공식 코드MixDiff: Mixing Natural and Synthetic Images for Robust Self-Supervised Representations
https://github.com/cs-giung/distill-latentbe
공식 코드Improving Ensemble Distillation With Weight Averaging and Diversifying Perturbation
https://github.com/cs-giung/lipsum-ft
공식 코드Lipsum-FT: Robust Fine-Tuning of Zero-Shot Models Using Random Text Guidance
https://github.com/cs-mshah/CDUL
공식 코드https://github.com/csarron/pumer
공식 코드PuMer: Pruning and Merging Tokens for Efficient Vision Language Models
https://github.com/csdllab/mca
공식 코드https://github.com/csiro-robotics/isice
공식 코드Learning Partial Correlation based Deep Visual Representation for Image Classification
https://github.com/csiro-robotics/l3dmc
공식 코드L3DMC: Lifelong Learning using Distillation via Mixed-Curvature Space
https://github.com/csjunjun/ric
공식 코드Recoverable Privacy-Preserving Image Classification through Noise-like Adversarial Examples
https://github.com/csm9493/L2UL
공식 코드Learning to Unlearn: Instance-wise Unlearning for Pre-trained Classifiers
https://github.com/csm9493/PNR
공식 코드Regularizing with Pseudo-Negatives for Continual Self-Supervised Learning
https://github.com/csong27/ml-model-remember
공식 코드https://github.com/cswluo/SEF
공식 코드Learning Semantically Enhanced Feature for Fine-Grained Image Classification
https://github.com/csyanbin/TPN-pytorch
공식 코드Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning
https://github.com/ctom2/latent-space-transform
공식 코드https://github.com/cugbrs/MLRSNet
공식 코드https://github.com/cuguilke/psykedelic
공식 코드A Deeper Look into Convolutions via Eigenvalue-based Pruning
https://github.com/cuibaby/dpk
공식 코드Better Teacher Better Student: Dynamic Prior Knowledge for Knowledge Distillation
https://github.com/cuihenggang/geeps
공식 코드MLtuner: System Support for Automatic Machine Learning Tuning
https://github.com/cumf/cumf_sgd
공식 코드https://github.com/cuong-pv/FAM-KD
공식 코드https://github.com/cure-lab/moodcat
공식 코드Out-of-Distribution Detection with Semantic Mismatch under Masking
https://github.com/custom-computing-ic/CNN-Based-Hyperspectral-Image-Classification
공식 코드Optimizing CNN-based Hyperspectral ImageClassification on FPGAs
https://github.com/custom-computing-ic/SpecPatConv3D-Network
공식 코드Convolution Based Spectral Partitioning Architecture for Hyperspectral Image Classification
https://github.com/cutz-j/FDFtNet
공식 코드FDFtNet: Facing Off Fake Images using Fake Detection Fine-tuning Network
https://github.com/cvi-szu/ccam
공식 코드https://github.com/cvi2snt/dda-mlic
공식 코드Discriminator-free Unsupervised Domain Adaptation for Multi-label Image Classification
https://github.com/cvjena/deic
공식 코드Tune It or Don't Use It: Benchmarking Data-Efficient Image Classification
https://github.com/cvl-umass/COSE
공식 코드COSE: A Consistency-Sensitivity Metric for Saliency on Image Classification
https://github.com/cvl-umass/PARTICLE
공식 코드PARTICLE: Part Discovery and Contrastive Learning for Fine-grained Recognition
https://github.com/cvl-umass/adaptclipzs
공식 코드Improved Zero-Shot Classification by Adapting VLMs with Text Descriptions
https://github.com/cvl-umass/counting-clusters
공식 코드Human-in-the-Loop Visual Re-ID for Population Size Estimation
https://github.com/cvl-umass/task2box
공식 코드Task2Box: Box Embeddings for Modeling Asymmetric Task Relationships
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.