[AAAI 2024] Prompt-based Distribution Alignment for Unsupervised Domain Adaptation
active 2023-12-14 → 2026-02-06 (UTC)
Activity over time
Daily event counts in the loaded window
Line chart, 786 days from 2023-12-14 to 2026-02-06. Pushes: 34 total, peak 19 in a day. Pull requests: 0 total, peak 0 in a day. Issues: 16 total, peak 5 in a day. Comments: 16 total, peak 4 in a day. Stars: 77 total, peak 5 in a day.
- Pushes
- Pull requests
- Issues
- Comments
- Stars
Stars, PRs, issues and forks are under-captured in the later part of this window. GH Archive progressively stopped capturing non-push events during 2026 — −95% or worse by the end of the window. Every series here except Pushes fades for that reason, so a decline above reflects the archive, not this repository. Pushes stay reliable throughout, so read them, and the contributor counts derived from them, as the real signal. Data health has the measurements.
Top contributors
Pushes, PRs, issues, reviews and comments — stars and forks excluded, so this is contribution rather than popularity
| Contributor | Contributions | Pushes | PRs | Comments |
|---|---|---|---|---|
| BaiShuanghao | 47 | 34 | 0 | 9 |
| leejq666 | 5 | 0 | 0 | 3 |
| 1d1x1w | 3 | 0 | 0 | 1 |
| 246dxw | 2 | 0 | 0 | 1 |
| lkh-meredith | 2 | 0 | 0 | 0 |
| leo-lab-511 | 2 | 0 | 0 | 1 |
| xingbw | 1 | 0 | 0 | 1 |
| 104343 | 1 | 0 | 0 | 0 |
| QYw12 | 1 | 0 | 0 | 0 |
| Panamera2333 | 1 | 0 | 0 | 0 |
| dw1360585641 | 1 | 0 | 0 | 0 |
Recent activity
Latest issues, pull requests and releases
- Issue#121043432025-01-03 08:53数据集划分
- Issue#111d1x1w2024-12-18 02:47你好，作者请问 论文的fig1和Analyzing the Metrics的代码实现能提供一下吗？
- Issue comment#101d1x1w2024-12-09 04:34在PDA代码中的Resnet视觉编码器，PDA/utils/clip_part.py中的ImageEncoder_Conv。我看里面并没有使用到视觉提示和深度视觉提示，文章中也没有提到相关的内容。请问文中base-resnet的数据都是无视觉提示跑出来的结果吗？
- Issue#3BaiShuanghao2024-12-08 07:27add a new dataset
- Issue#2BaiShuanghao2024-12-08 07:27A very interesting job. What is the partitioning method for the dataset? What is the ratio of training set to test set? For example, office_home
- Issue#1BaiShuanghao2024-12-08 07:27About train.py
- Issue comment#9BaiShuanghao2024-12-08 07:26RuntimeError: "addmm_impl_cpu_" not implemented for 'Half'
- Issue#10BaiShuanghao2024-12-08 07:25在PDA代码中的Resnet视觉编码器，PDA/utils/clip_part.py中的ImageEncoder_Conv。我看里面并没有使用到视觉提示和深度视觉提示，文章中也没有提到相关的内容。请问文中base-resnet的数据都是无视觉提示跑出来的结果吗？
- Issue comment#10BaiShuanghao2024-12-08 07:24在PDA代码中的Resnet视觉编码器，PDA/utils/clip_part.py中的ImageEncoder_Conv。我看里面并没有使用到视觉提示和深度视觉提示，文章中也没有提到相关的内容。请问文中base-resnet的数据都是无视觉提示跑出来的结果吗？
- Issue#101d1x1w2024-12-08 03:34在PDA代码中的Resnet视觉编码器，PDA/utils/clip_part.py中的ImageEncoder_Conv。我看里面并没有使用到视觉提示和深度视觉提示，文章中也没有提到相关的内容。请问文中base-resnet的数据都是无视觉提示跑出来的结果吗？
- Issue#9QYw122024-11-05 07:35RuntimeError: "addmm_impl_cpu_" not implemented for 'Half'
- Issue#8dw13605856412024-09-26 17:08train.py中import导入错误have error，from dassl.data.datasets import OfficeHome, VisDA17, Office31
- Issue#7lkh-meredith2024-08-04 12:30Vision encoder 基于ResNet的实现
- Issue#7lkh-meredith2024-08-04 12:10Vision encoder 基于ResNet的实现
- Issue comment#6BaiShuanghao2024-07-26 19:34office31在第14个epoch之后accuracy变为3%左右，请问怎么解决
- Issue comment#5leo-lab-5112024-07-25 13:29office31 a-w epoch=13-20 3%
- Issue#6leo-lab-5112024-07-25 13:27office31在第14个epoch之后accuracy变为3%左右，请问怎么解决
- Issue comment#5246dxw2024-05-26 02:53office31 a-w epoch=13-20 3%
- Issue#5246dxw2024-05-26 02:51office31 a-w epoch=13-20 3%
- Issue comment#4BaiShuanghao2024-05-23 09:12visualization
- Issue comment#4xingbw2024-05-23 06:31visualization
- Issue#4Panamera23332024-05-13 10:01visualization
- Issue comment#3BaiShuanghao2024-03-28 02:27add a new dataset
- Issue comment#2BaiShuanghao2024-03-26 14:02A very interesting job. What is the partitioning method for the dataset? What is the ratio of training set to test set? For example, office_home
- Issue#2leejq6662024-03-26 08:15A very interesting job. What is the partitioning method for the dataset? What is the ratio of training set to test set? For example, office_home
Totals cover only the window loaded into ClickHouse and count events, not GitHub's lifetime totals — 77 stars here means stars gained during the window, not the repo's star count.