64 papers with code • 21 benchmarks • 19 datasets

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Most implemented papers

Deep Residual Learning for Image Recognition

KaimingHe/resnet-1k-layers CVPR 2016

Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.

An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

google-research/vision_transformer ICLR 2021

While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited.

PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space

yanx27/Pointnet_Pointnet2_pytorch NeurIPS 2017

By exploiting metric space distances, our network is able to learn local features with increasing contextual scales.

Generative Adversarial Text to Image Synthesis

reedscot/icml2016 17 May 2016

Automatic synthesis of realistic images from text would be interesting and useful, but current AI systems are still far from this goal.

Llama 2: Open Foundation and Fine-Tuned Chat Models

facebookresearch/llama 18 Jul 2023

In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters.

A Tutorial on Principal Component Analysis

maxhalford/prince 3 Apr 2014

Principal component analysis (PCA) is a mainstay of modern data analysis - a black box that is widely used but (sometimes) poorly understood.

Qwen2.5 Technical Report

qwenlm/qwen2.5 19 Dec 2024

In addition, for hosted solutions, the proprietary models currently include two mixture-of-experts (MoE) variants: Qwen2. 5-Turbo and Qwen2. 5-Plus, both available from Alibaba Cloud Model Studio.

Real-world adversarial attack on MTCNN face detection system

edosedgar/mtcnnattack 14 Oct 2019

Recent studies proved that deep learning approaches achieve remarkable results on face detection task.

Anomaly Detection via Reverse Distillation from One-Class Embedding

hq-deng/RD4AD CVPR 2022

Knowledge distillation (KD) achieves promising results on the challenging problem of unsupervised anomaly detection (AD). The representation discrepancy of anomalies in the teacher-student (T-S) model provides essential evidence for AD.

Entangled Watermarks as a Defense against Model Extraction

cleverhans-lab/entangled-watermark 27 Feb 2020

Such pairs are watermarks, which are not sampled from the task distribution and are only known to the defender.