https://github.com/csguoh/fastvar
FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation
A Biologically Plausible Learning Rule for Deep Learning in the Brain
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets
MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretraining
Classification-Specific Parts for Improving Fine-Grained Visual Categorization
Seesaw-Net: Convolution Neural Network With Uneven Group Convolution
Seesaw-Net: Convolution Neural Network With Uneven Group Convolution
Extremely Lightweight Quantization Robust Real-Time Single-Image Super Resolution for Mobile Devices
Tube Convolutional Neural Network (T-CNN) for Action Detection in Videos
LayoutLM: Pre-training of Text and Layout for Document Image Understanding
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and Tree
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
A Simple Framework for Contrastive Learning of Visual Representations
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.