Popular GitHub Repositories for Gr Adapt

:stuck_out_tongue_closed_eyes: TensorFlowTTS: Real-Time State-of-the-art Speech Synthesis for Tensorflow 2 (supported including English, French, Korean, Chinese, German and Easy to adapt for other languages)

micronet, a model compression and deploy lib. compression: 1、quantization: quantization-aware-training(QAT), High-Bit(>2b)(DoReFa/Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference)、Low-Bit(≤2b)/Ternary and Binary(TWN/BNN/XNOR-Net); post-training-quantization(PTQ), 8-bit(tensorrt); 2、 pruning: normal、regular and group convolutional channel pruning; 3、 group convolution structure; 4、batch-normalization fuse for quantization. deploy: tensorrt, fp32/fp16/int8(ptq-calibration)、op-adapt(upsample)、dynamic_shape

Learning to Adapt Structured Output Space for Semantic Segmentation, CVPR 2018 (spotlight)

Hypernetworks that adapt LLMs for specific benchmark tasks using only textual task description as the input

? [BMVC 2022] You Only Need 90K Parameters to Adapt Light: A Light Weight Transformer for Image Enhancement and Exposure Correction. SOTA for low light enhancement, 0.004 seconds try this for pre-processing.

Helps you write algorithms in PyTorch that adapt to the available (CUDA) memory

Code for "Show, Adapt and Tell: Adversarial Training of Cross-domain Image Captioner" in ICCV 2017

Implementation of "Generate To Adapt: Aligning Domains using Generative Adversarial Networks"

This RAG (Retrieval-Augmented Generation) project is implemented using pure Java. This approach makes it easier to adapt to enterprise-level environments and is more conducive to secondary development.

Baxter Pick and Place using GR-ConvNet. IROS 2020.

[IEEE-TMI 2021] This is our PyTorch implementation for Adapt Everywhere paper on unsupervised domain adaptation using entropy and point-cloud paper.

ACL 2022: Adaptor: a library to easily adapt a language model to your own task, domain, or custom objective(s).

This repository contains the implementation of Label-Free XAI, a new framework to adapt explanation methods to unsupervised models. For more details, please read our ICML 2022 paper: 'Label-Free Explainability for Unsupervised Models'.

A powerful tool that converts voice recordings into high-quality Anki flashcards using AI-powered transcription and LLM processing, featuring Few-Shot Learning to adapt to your personal style and supporting any language or subject matter.

adapt data to and from every format

Test-time adaptation for speech recognition model by single utterance. The official implementation of "Listen, Adapt, Better WER: Source-free Single-utterance Test-time Adaptation for Automatic Speech Recognition" paper.

Transfer learning in deep reinforcement learning for continuous control. Implemented DDPG and TD3 algorithms and evaluated ability to adapt to changes in environment dynamics and new environments

A open-source framework designed to adapt pre-trained Language Models (LLMs), such as Llama, Mistral, and Mixtral, to a wide array of domains and languages.

WhisperMesh is an advanced chatbot that integrates voice and text interactions, delivering personalized responses through LLM models and a sophisticated vector database. Leveraging the RAG framework from Haystack, it ensures engaging, data-driven conversations that adapt to your preferred style.

AscendAI is a local-first, self-mutating AGI skeleton—rigged with sovereign agents, recursive autonomy, and a will to adapt. GremlinGPT is its chaotic heart: learning, building, and evolving into AscendAI as it survives testing and bonds with its user. Not just a wrapper—a battle map with agency.