https://github.com/vgcq/easier
공식 코드The Simpler The Better: An Entropy-Based Importance Metric To Reduce Neural Networks' Depth
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/vgsatorras/few-shot-gnn
공식 코드https://github.com/vgtomahawk/LR_GC_OOD
공식 코드https://github.com/vicentealex/spiking_resnet
공식 코드Keys to Accurate Feature Extraction Using Residual Spiking Neural Networks
https://github.com/vico-uoe/universalrepresentations
공식 코드Universal Representations: A Unified Look at Multiple Task and Domain Learning
https://github.com/victor-yifanwu/Outlier-Exposure-by-Simple-Transformations
공식 코드Revisiting Energy-Based Model for Out-of-Distribution Detection
https://github.com/vidit09/domaingen
공식 코드CLIP the Gap: A Single Domain Generalization Approach for Object Detection
https://github.com/vidmodex/vidmodex
공식 코드VidModEx: Interpretable and Efficient Black Box Model Extraction for High-Dimensional Spaces
https://github.com/viethoang1512/glot
공식 코드https://github.com/viharoszsolt/idenas
공식 코드https://github.com/vikasverma1077/ICT
공식 코드Interpolation Consistency Training for Semi-Supervised Learning
https://github.com/vikasverma1077/manifold_mixup
공식 코드Manifold Mixup: Better Representations by Interpolating Hidden States
https://github.com/vila-lab/od3
공식 코드OD3: Optimization-free Dataset Distillation for Object Detection
https://github.com/vimar-gu/msinet
공식 코드MSINet: Twins Contrastive Search of Multi-Scale Interaction for Object ReID
https://github.com/vimslab/sodawidenetplusplus
공식 코드SODAWideNet++: Combining Attention and Convolutions for Salient Object Detection
https://github.com/vinayprabhu/Kannada_MNIST
공식 코드Kannada-MNIST: A new handwritten digits dataset for the Kannada language
https://github.com/vinojjayasundara/textcaps
공식 코드TextCaps : Handwritten Character Recognition with Very Small Datasets
https://github.com/vinuni-vishc/Few-Shot-Cosine-Transformer
공식 코드Enhancing Few-shot Image Classification with Cosine Transformer
https://github.com/vinuni-vishc/cg-imif
공식 코드Multi-stream Fusion for Class Incremental Learning in Pill Image Classification
https://github.com/vinusankars/curriculum_learning
공식 코드Statistical Measures For Defining Curriculum Scoring Function
https://github.com/vipailab/vmf_op
공식 코드https://github.com/virajprabhu/clue
공식 코드Active Domain Adaptation via Clustering Uncertainty-weighted Embeddings
https://github.com/virobo-15/xm-gan
공식 코드Cross-modulated Few-shot Image Generation for Colorectal Tissue Classification
https://github.com/virtuosoresearch/noise-stability-optimization
공식 코드Noise Stability Optimization for Finding Flat Minima: A Hessian-based Regularization Approach
https://github.com/virtuosoresearch/robust-fine-tuning
공식 코드Robust Fine-Tuning of Deep Neural Networks with Hessian-based Generalization Guarantees
https://github.com/virusdoll/active-negative-loss
공식 코드Active Negative Loss: A Robust Framework for Learning with Noisy Labels
https://github.com/vishalned/MMEarth-data
공식 코드MMEarth: Exploring Multi-Modal Pretext Tasks For Geospatial Representation Learning
https://github.com/vishalned/MMEarth-train
공식 코드MMEarth: Exploring Multi-Modal Pretext Tasks For Geospatial Representation Learning
https://github.com/vision-sjtu/quadmamba
공식 코드QuadMamba: Learning Quadtree-based Selective Scan for Visual State Space Model
https://github.com/visionlearninggroup/snd
공식 코드Tune it the Right Way: Unsupervised Validation of Domain Adaptation via Soft Neighborhood Density
https://github.com/visiontao/cl
공식 코드https://github.com/visiontao/pfcl
공식 코드Prior-Free Continual Learning with Unlabeled Data in the Wild
https://github.com/visionxlab/lrs-vqa
공식 코드https://github.com/visipedia/fg_geo
공식 코드https://github.com/visipedia/imat_fashion_comp
공식 코드https://github.com/visresearch/LLaVA-STF
공식 코드Learning Compact Vision Tokens for Efficient Large Multimodal Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.