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[NeurIPS 2023] Latent Graph Inference with Limited Supervision
This is a collection of awesome papers I have read (carefully or roughly) in the fields of computer vision, machine learning, pattern recognition, and data mining (where the notes only represent my…
Unified Controllable Visual Generation Model
Awesome papers on 3D anomaly detection.
[Preprint] Why is the State of Neural Network Pruning so Confusing? On the Fairness, Comparison Setup, and Trainability in Network Pruning
[CVPR 2023] Real-Time Neural Light Field on Mobile Devices
[NeurIPS'22] What Makes a "Good" Data Augmentation in Knowledge Distillation -- A Statistical Perspective
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
[CIKM 2022] Self-supervision Meets Adversarial Perturbation: A Novel Framework for Anomaly Detection (PyTorch)
[ICLR'23] Trainability Preserving Neural Pruning (PyTorch)
A curated list of papers of interesting empirical study and insight on deep learning. Continually updating...
[ECCV 2022] R2L: Distilling Neural Radiance Field to Neural Light Field for Efficient Novel View Synthesis
[ICLR 2022 poster] Official PyTorch implementation of "Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework"
[ICLR'22] PyTorch code for our paper "Learning Efficient Image Super-Resolution Networks via Structure-Regularized Pruning"
[IJCAI'22 Survey] Recent Advances on Neural Network Pruning at Initialization.
[ICLR'21] Neural Pruning via Growing Regularization (PyTorch)
[CVPR'20] Collaborative Distillation for Ultra-Resolution Universal Style Transfer (PyTorch)
Collection of recent methods on (deep) neural network compression and acceleration.
[NeurIPS'21 Spotlight] Aligned Structured Sparsity Learning for Efficient Image Super-Resolution (PyTorch)
A generic code base for neural network pruning, especially for pruning at initialization.
Python logging package for easy reproducible experimenting in research