Calligrapher: Freestyle Text Image Customization
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Updated
Sep 3, 2025 - Python
Calligrapher: Freestyle Text Image Customization
A pytorch implementation of paper 'Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillation', https://arxiv.org/abs/1905.08094
Deep Hash Distillation for Image Retrieval - ECCV 2022
[CVPR 2025] Official PyTorch implementation of MaskSub "Masking meets Supervision: A Strong Learning Alliance"
(NeurIPS 2025 π₯) Official implementation for "Efficient Multi-modal Large Language Models via Progressive Consistency Distillation"
(Unofficial) Data-Distortion Guided Self-Distillation for Deep Neural Networks (AAAI 2019)
Bayesian Optimization Meets Self-Distillation, ICCV 2023
Self-Distillation and Knowledge Distillation Experiments with PyTorch.
Pytorch implementation of "Emerging Properties in Self-Supervised Vision Transformers" (a.k.a. DINO)
A minimalist unofficial implementation of "Self-Distillation from the Last Mini-Batch for Consistency Regularization"
Modality-Agnostic Learning for Medical Image Segmentation Using Multi-modality Self-distillation
A generalized self-supervised training paradigm for unimodal and multimodal alignment and fusion.
Official implementation of Self-Distillation for Gaussian Processes
A simple and efficient implementation of Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture (I-JEPA)
SEED: A Transformers-Based Autoencoder Enhanced by Masking and Self-Distillation for Business Process Anomaly Detection
Create stunning text images with Calligrapher, a diffusion-based framework for customizable digital calligraphy. Explore your creativity on GitHub! ππ©π»
Self supervised learning through self distillation with no labels (DINO) with Vision Transformers on the PCAM dataset.
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