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100-days-of-ml-code

100 Days of ML Coding

2021-cvpr-mvcln

PyTorch implementation for Partially View-aligned Representation Learning with Noise-robust Contrastive Loss (CVPR 2021)

3dgnn_pytorch

3D Graph Neural Networks for RGBD Semantic Segmentation

aaai2023-pvd

Official Implementation of PVD:One is All: Bridging the Gap Between Neural Radiance Fields Architectures with Progressive Volume Distillation

acne

Code Release for CVPR 2020, "ACNe: Attentive Context Normalizationfor Robust Permutation-Equivariant Learning"

active-passive-losses

Code for ICML2020 paper [“Normalized Loss Functions for Deep Learning with Noisy Labels"] https://arxiv.org/abs/2006.13554

activitygraph_transformer

actnn

ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training

adab2n

Official Implementation of NeurIPS 2023 paper "Overcoming Recency Bias of Normalization Statistics in Continual Learning: Balance and Adaptation"

adabelief-optimizer

Repository for NeurIPS 2020 Spotlight "AdaBelief Optimizer: Adapting stepsizes by the belief in observed gradients"

adadfq

adainf

Official code for ICLR 2024 paper Do Generated Data Always Help Contrastive Learning?

adamp

Slowing Down the Weight Norm Increase in Momentum-based Optimizers

adapool

exponential adaptive pooling for PyTorch

adaptively-customizing-activation-functions

To enhance the nonlinearity of neural networks and increase their mapping abilities between the inputs and response variables, activation functions play a crucial role to model more complex relationships and patterns in the data. In this work, a novel methodology is proposed to adaptively customize activation functions only by adding very few parameters to the traditional activation functions such as Sigmoid, Tanh, and ReLU. To verify the effectiveness of the proposed methodology, some theoretical and experimental analysis on accelerating the convergence and improving the performance is presented, and a series of experiments are conducted based on various network models (such as AlexNet, VGGNet, GoogLeNet, ResNet and DenseNet), and various datasets (such as CIFAR10, CIFAR100, miniImageNet, PASCAL VOC and COCO) . To further verify the validity and suitability in various optimization strategies and usage scenarios, some comparison experiments are also implemented among different optimization strategies (such as SGD, Momentum, AdaGrad, AdaDelta and ADAM) and different recognition tasks like classification and detection. The results show that the proposed methodology is very simple but with significant performance in convergence speed, precision and generalization, and it can surpass other popular methods like ReLU and adaptive functions like Swish in almost all experiments in terms of overall performance.

adaptively-parametric-relu

The adaptively parametric ReLU is an activation function that performs non-identically for input samples.

adatriplet-ra

addernet

Code for paper " AdderNet: Do We Really Need Multiplications in Deep Learning?"

ade-czsl

[CVPR 2023] Learning Attention as Disentangler for Compositional Zero-shot Learning

admrl

Code for paper "Model-based Adversarial Meta-Reinforcement Learning" (https://arxiv.org/abs/2006.08875)

adv-ss-pretraining

[CVPR 2020] Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning

adversarial

Code and hyperparameters for the paper "Generative Adversarial Networks"

adversarial-continual-learning

Implementation for the paper "Adversarial Continual Learning" in PyTorch.

adversarial-contrastive-learning

[NeurIPS 2020] “Adversarial Contrastive Learning: Harvesting More Robustness from Unsupervised Pre-Training”, Ziyu Jiang, Tianlong Chen, Ting Chen, Zhangyang Wang

adversarialnetspapers

The classical papers and codes about generative adversarial nets

agd

[ICML2020] "AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks" by Yonggan Fu, Wuyang Chen, Haotao Wang, Haoran Li, Yingyan Lin, Zhangyang Wang

agvm

Large-batch Optimization for Dense Visual Predictions (NeurIPS 2022)

ai-deadlines

:alarm_clock: AI conference deadline countdowns

ai-expert-roadmap

Roadmap to becoming an Artificial Intelligence Expert in 2021

aim-fewshot-continual