https://github.com/seyrankhademi/AmsterTime
공식 코드AmsterTime: A Visual Place Recognition Benchmark Dataset for Severe Domain Shift
Papers with Code 아카이브
Image Classification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 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/sflippl/multi-task
공식 코드Inductive biases of multi-task learning and finetuning: multiple regimes of feature reuse
https://github.com/sg-nm/cgp-cnn
공식 코드A Genetic Programming Approach to Designing Convolutional Neural Network Architectures
https://github.com/sgchr273/LPLgrad
공식 코드https://github.com/sgchr273/fishermask
공식 코드https://github.com/sgvaze/generalized-category-discovery
공식 코드https://github.com/shabbeersh/Impact-of-FC-layers
공식 코드https://github.com/shadealsha/ltr-weight-balancing
공식 코드https://github.com/shaharlutatipersonal/ocd
공식 코드https://github.com/shaier/comet
공식 코드https://github.com/shaiqmalik/Teacher-Class
공식 코드Teacher-Class Network: A Neural Network Compression Mechanism
https://github.com/shamaeem/lnsqrtstepsize
공식 코드Modified Step Size for Enhanced Stochastic Gradient Descent: Convergence and Experiments
https://github.com/shambhavicodes/miscalibration-ssl
공식 코드Do not trust what you trust: Miscalibration in Semi-supervised Learning
https://github.com/shamim-hussain/ssa
공식 코드The Information Pathways Hypothesis: Transformers are Dynamic Self-Ensembles
https://github.com/shangtse/robust-physical-attack
공식 코드ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector
https://github.com/shangxinyi/fedic
공식 코드FEDIC: Federated Learning on Non-IID and Long-Tailed Data via Calibrated Distillation
https://github.com/shanshuo/R2-MLP
공식 코드https://github.com/shantanuj/tdam_top_down_attention_module
공식 코드TDAM: Top-Down Attention Module for Contextually Guided Feature Selection in CNNs
https://github.com/shaoruchen/deepsplit
공식 코드DeepSplit: Scalable Verification of Deep Neural Networks via Operator Splitting
https://github.com/shaoshuo-ss/eaaw
공식 코드https://github.com/sharat29ag/CDAL
공식 코드https://github.com/sharc-lab/rapid-inr
공식 코드Rapid-INR: Storage Efficient CPU-free DNN Training Using Implicit Neural Representation
https://github.com/sharifhasani/coviddetection
공식 코드Automated detection of COVID-19 cases from chest X-ray images using deep neural network and XGBoost
https://github.com/sharonpeled/psa-mil
공식 코드https://github.com/sharpenb/Posterior-Network
공식 코드Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts
https://github.com/shashankkotyan/DualQualityAssessment
공식 코드Adversarial Robustness Assessment: Why both $L_0$ and $L_\infty$ Attacks Are Necessary
https://github.com/shashankkotyan/EvoSeed
공식 코드Breaking Free: How to Hack Safety Guardrails in Black-Box Diffusion Models!
https://github.com/shashankskagnihotri/adv-corrected-ddcat-cospgd
공식 코드CosPGD: an efficient white-box adversarial attack for pixel-wise prediction tasks
https://github.com/shashankskagnihotri/benchmarking_reliability_generalization
공식 코드SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification
https://github.com/shashankskagnihotri/cospgd
공식 코드CosPGD: an efficient white-box adversarial attack for pixel-wise prediction tasks
https://github.com/shaunak27/grain-clip
공식 코드Grounding Descriptions in Images informs Zero-Shot Visual Recognition
https://github.com/shaunak27/hepco-fed
공식 코드Continual Adaptation of Vision Transformers for Federated Learning
https://github.com/shellysheynin/locally-sag-transformer
공식 코드https://github.com/shendu-sw/deep-manifold-embedding
공식 코드Deep Manifold Embedding for Hyperspectral Image Classification
https://github.com/shendu-sw/hyperdid
공식 코드HyperDID: Hyperspectral Intrinsic Image Decomposition with Deep Feature Embedding
https://github.com/shendu-sw/noise-physical-framework
공식 코드A CNN with Noise Inclined Module and Denoise Framework for Hyperspectral Image Classification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.