https://github.com/myingysun/sgdph
공식 코드SGD with Partial Hessian for Deep Neural Networks Optimization
Papers with Code 아카이브
Image Classification
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- 평가표
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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/mzhaoshuai/Divide-and-Co-training
공식 코드Towards Better Accuracy-efficiency Trade-offs: Divide and Co-training
https://github.com/mzhaoshuai/rlcf
공식 코드Test-Time Adaptation with CLIP Reward for Zero-Shot Generalization in Vision-Language Models
https://github.com/mzweilin/EvadeML-Zoo
공식 코드https://github.com/nab170130/new_streamline
공식 코드STREAMLINE: Streaming Active Learning for Realistic Multi-Distributional Settings
https://github.com/najeebkhan/sparseout
공식 코드https://github.com/nalinda05kl/nonlinear_activations
공식 코드Effects of the Nonlinearity in Activation Functions on the Performance of Deep Learning Models
https://github.com/name-is-what/sego
공식 코드Structural Entropy Guided Unsupervised Graph Out-Of-Distribution Detection
https://github.com/namhoonlee/snip-public
공식 코드SNIP: Single-shot Network Pruning based on Connection Sensitivity
https://github.com/namhoonlee/spp-public
공식 코드A Signal Propagation Perspective for Pruning Neural Networks at Initialization
https://github.com/namkoong-lab/disde
공식 코드https://github.com/naver-ai/augsub
공식 코드https://github.com/naver-ai/cmo
공식 코드https://github.com/naver-ai/dtm
공식 코드https://github.com/naver-ai/lut
공식 코드Learning with Unmasked Tokens Drives Stronger Vision Learners
https://github.com/naver-ai/negmerge
공식 코드NegMerge: Consensual Weight Negation for Strong Machine Unlearning
https://github.com/naver-ai/pit
공식 코드https://github.com/naver-ai/rdnet
공식 코드https://github.com/naver-ai/relabel_imagenet
공식 코드Re-labeling ImageNet: from Single to Multi-Labels, from Global to Localized Labels
https://github.com/naver-ai/vidt
공식 코드ViDT: An Efficient and Effective Fully Transformer-based Object Detector
https://github.com/naver/croco
공식 코드CroCo: Self-Supervised Pre-training for 3D Vision Tasks by Cross-View Completion
https://github.com/naver/relis
공식 코드Reliability in Semantic Segmentation: Are We on the Right Track?
https://github.com/nayeemrizve/invariance-equivariance
공식 코드Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot Learning
https://github.com/nayeemrizve/openldn
공식 코드OpenLDN: Learning to Discover Novel Classes for Open-World Semi-Supervised Learning
https://github.com/nayeemrizve/trssl
공식 코드https://github.com/nayeemrizve/ups
공식 코드https://github.com/nazim1021/OOD-detection-using-OECC
공식 코드Outlier Exposure with Confidence Control for Out-of-Distribution Detection
https://github.com/nazmul-karim170/NFT-Augmented-Backdoor-Purification
공식 코드Augmented Neural Fine-Tuning for Efficient Backdoor Purification
https://github.com/nazmul-karim170/unicon-noisy-label
공식 코드UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learning
https://github.com/nazmul729/representationalaxialnet
공식 코드Improving Axial-Attention Network Classification via Cross-Channel Weight Sharing
https://github.com/nazya/kate
공식 코드Remove that Square Root: A New Efficient Scale-Invariant Version of AdaGrad
https://github.com/nblt/twa
공식 코드Trainable Weight Averaging: A General Approach for Subspace Training
https://github.com/ndb796/multipleinputmixup
공식 코드Towards Reliable AI Model Deployments: Multiple Input Mixup for Out-of-Distribution Detection
https://github.com/ndminhkhoi46/asRNN
공식 코드https://github.com/neeleshg23/packd
공식 코드PaCKD: Pattern-Clustered Knowledge Distillation for Compressing Memory Access Prediction Models
https://github.com/neilfei/brivl-nmi
공식 코드Towards artificial general intelligence via a multimodal foundation model
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.