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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://bitbucket.org/YanivShu/binary_weighted_networks_public
공식 코드Exact Backpropagation in Binary Weighted Networks with Group Weight Transformations
https://bitbucket.org/YanivShu/diffprune_public
공식 코드https://bitbucket.org/aauvap/sewer-ml
공식 코드Sewer-ML: A Multi-Label Sewer Defect Classification Dataset and Benchmark
https://bitbucket.org/catk1ns0n/repr_public
공식 코드Pseudo-Rehearsal: Achieving Deep Reinforcement Learning without Catastrophic Forgetting
https://bitbucket.org/chhshen/fishercoding
공식 코드Compositional Model based Fisher Vector Coding for Image Classification
https://bitbucket.org/nelliottrosa/base_fairness
공식 코드A Differentiable Distance Approximation for Fairer Image Classification
https://bitbucket.org/raghavian/lotenet_pytorch
공식 코드https://bitbucket.org/raingo-ur/mll-tf
공식 코드Improving Pairwise Ranking for Multi-label Image Classification
https://git.tu-berlin.de/rsim/CCML
공식 코드https://git.tu-berlin.de/rsim/RCML
공식 코드Multi-Label Noise Robust Collaborative Learning for Remote Sensing Image Classification
https://git.tu-berlin.de/rsim/reben-training-scripts
공식 코드reBEN: Refined BigEarthNet Dataset for Remote Sensing Image Analysis
https://github.com/0xfafa/fa
공식 코드FA: Forced Prompt Learning of Vision-Language Models for Out-of-Distribution Detection
https://github.com/0xpranjal/DR-Detection-using-Swarm-intelligence
공식 코드https://github.com/0zgur0/STAR_Network
공식 코드https://github.com/17Skye17/VideoLT
공식 코드https://github.com/1998v7/item
공식 코드https://github.com/1998v7/self-filtering
공식 코드Self-Filtering: A Noise-Aware Sample Selection for Label Noise with Confidence Penalization
https://github.com/1adrianb/binary-networks-pytorch
공식 코드https://github.com/1e12Leon/RemoteTrimmer
공식 코드RemoteTrimmer: Adaptive Structural Pruning for Remote Sensing Image Classification
https://github.com/2004-0813/hmdrn-main
공식 코드https://github.com/2014mchidamb/how-flawed-is-ece
공식 코드https://github.com/2014mchidamb/midpoint-mixup-multi-view-icml
공식 코드Provably Learning Diverse Features in Multi-View Data with Midpoint Mixup
https://github.com/2014mchidamb/temp-scaling-limitations
공식 코드On the Limitations of Temperature Scaling for Distributions with Overlaps
https://github.com/2771096196/RepAct
공식 코드https://github.com/2obe/feature-distance-loss
공식 코드Discriminative Feature Learning through Feature Distance Loss
https://github.com/2younis/DFGRID
공식 코드Data-Free Generative Replay for Class-Incremental Learning on Imbalanced Data
https://github.com/2younis/dfgr
공식 코드Data-Free Generative Replay for Class-Incremental Learning on Imbalanced Data
https://github.com/594422814/CF-VGG
공식 코드Real-Time Correlation Tracking via Joint Model Compression and Transfer
https://github.com/60972823l/SSL-DNLL
공식 코드Semi-Supervised Learning with Pseudo-Negative Labels for Image Classification
https://github.com/AFAgarap/cnn-svm
공식 코드https://github.com/AFAgarap/gru-svm
공식 코드https://github.com/AI-EXP-group/D-ADD
공식 코드Defense Against Model Stealing Based on Account-Aware Distribution Discrepancy
https://github.com/AI-secure/multi-task-learning
공식 코드Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective Adaptation
https://github.com/AIRI-Institute/GLiRA
공식 코드GLiRA: Black-Box Membership Inference Attack via Knowledge Distillation
https://github.com/AKASH2907/End-to-End-Semi-Supervised-Learning-for-Video-Action-Detection
공식 코드End-to-End Semi-Supervised Learning for Video Action Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.