Alternatives and similar repositories for dream-ood

Users that are interested in dream-ood are comparing it to the libraries listed below

• lisadunlap / ALIA
  Augmenting with Language-guided Image Augmentation (ALIA)
  ☆77Updated last year
• GeorgeCazenavette / glad
  ☆114Updated 2 years ago
• yu-rp / Distribution-Shift-Iverson
  ☆42Updated last year
• yongchaoz / diffusion_inversion
  Code for "Training on Thin Air: Improve Image Classification with Generated Data"
  ☆47Updated 2 years ago
• clear-nus / selective-amnesia
  ☆63Updated 9 months ago
• changdaeoh / BlackVIP
  Official implementation for CVPR'23 paper "BlackVIP: Black-Box Visual Prompting for Robust Transfer Learning"
  ☆111Updated last year
• virajprabhu / LANCE
  LANCE: Stress-testing Visual Models by Generating Language-guided Counterfactual Images
  ☆32Updated last year
• mihirp1998 / Diffusion-TTA
  Diffusion-TTA improves pre-trained discriminative models such as image classifiers or segmentors using pre-trained generative models.
  ☆74Updated last year
• deeplearning-wisc / npos
  source code for ICLR'23 paper "Non-parametric Outlier Synthesis"
  ☆53Updated last year
• zycheiheihei / Transferable-Visual-Prompting
  [CVPR2024 Highlight] Official implementation for Transferable Visual Prompting. The paper "Exploring the Transferability of Visual Prompt…
  ☆44Updated 6 months ago
• zhangq327 / U-MAE
  Official Code for NeurIPS 2022 Paper: How Mask Matters: Towards Theoretical Understandings of Masked Autoencoders
  ☆67Updated last year
• yongchaoz / FRePo
  Official Code for Dataset Distillation using Neural Feature Regression (NeurIPS 2022)
  ☆47Updated 2 years ago
• Hritikbansal / generative-robustness
  Create generated datasets and train robust classifiers
  ☆36Updated last year
• ML-GSAI / DPT
  Official PyTorch implementation for "Diffusion Models and Semi-Supervised Learners Benefit Mutually with Few Labels"
  ☆94Updated last year
• taeckyung / SoTTA
  This is the official PyTorch Implementation of "SoTTA: Robust Test-Time Adaptation on Noisy Data Streams (NeurIPS '23)" by Taesik Gong*, …
  ☆22Updated last year
• mlvlab / DAPT
  Distribution-Aware Prompt Tuning for Vision-Language Models (ICCV 2023)
  ☆41Updated last year
• mzhaoshuai / RLCF
  [ICLR 2024] Test-Time Adaptation with CLIP Reward for Zero-Shot Generalization in Vision-Language Models.
  ☆82Updated 11 months ago
• OPTML-Group / ILM-VP
  [CVPR23] "Understanding and Improving Visual Prompting: A Label-Mapping Perspective" by Aochuan Chen, Yuguang Yao, Pin-Yu Chen, Yihua Zha…
  ☆53Updated last year
• MediaBrain-SJTU / OC_LT
  Official code base for "Long-Tailed Diffusion Models With Oriented Calibration" ICLR2024
  ☆14Updated last year
• locuslab / FLYP
  Code for Finetune like you pretrain: Improved finetuning of zero-shot vision models
  ☆100Updated last year
• BAAI-DCAI / Training-Data-Synthesis
  [ICLR 2024] Real-Fake: Effective Training Data Synthesis Through Distribution Matching
  ☆79Updated last year
• LINs-lab / RDED
  [CVPR 2024] On the Diversity and Realism of Distilled Dataset: An Efficient Dataset Distillation Paradigm
  ☆71Updated 4 months ago
• vimar-gu / MinimaxDiffusion
  [CVPR2024] Efficient Dataset Distillation via Minimax Diffusion
  ☆94Updated last year
• shiyegao / DDA
  Official repository of "Back to Source: Diffusion-Driven Test-Time Adaptation"
  ☆80Updated last year
• princetonvisualai / multimodal_dataset_distillation
  ☆56Updated 6 months ago
• showlab / datacentric.vlp
  Compress conventional Vision-Language Pre-training data
  ☆51Updated last year
• UCSC-VLAA / EVP
  [TMLR'24] This repository includes the official implementation our paper "Unleashing the Power of Visual Prompting At the Pixel Level"
  ☆41Updated last year
• jusiro / CLAP
  [CVPR'24] Validation-free few-shot adaptation of CLIP, using a well-initialized Linear Probe (ZSLP) and class-adaptive constraints (CLAP)…
  ☆74Updated last month
• cvlab-columbia / DoubleRight
  ☆27Updated last year
• ZhangYuanhan-AI / visual_prompt_retrieval
  [NeurIPS2023] Official implementation and model release of the paper "What Makes Good Examples for Visual In-Context Learning?"
  ☆177Updated last year