Unsupervised Domain Adaptation
Unsupervised Domain Adaptation is a learning framework to transfer knowledge learned from source domains with a large number of annotated training examples to target domains with unlabeled data only.
Source: Domain-Specific Batch Normalization for Unsupervised Domain Adaptation
Papers
Showing 51–75 of 1951 papers
All datasetsDuke to MarketMarket to DukeCityscapes-to-Foggy CityscapesOffice-HomeMarket to MSMTImageNet-CSYNTHIA-to-CityscapesVehicleID to VeRi-776Duke to MSMTSIM10K to CityscapesVeri-776 to VehicleID LargeVeri-776 to VehicleID Medium
Benchmark Results
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | ALDI++(Resnet50+FPN) | [email protected] | 66.8 | — | Unverified |
| 2 | RT-DATR(640x640, real-time) | [email protected] | 52.7 | — | Unverified |
| 3 | MRT | [email protected] | 51.2 | — | Unverified |
| 4 | DDT | [email protected] | 50 | — | Unverified |
| 5 | MIC | [email protected] | 47.6 | — | Unverified |
| 6 | O2net | [email protected] | 46.8 | — | Unverified |
| 7 | LGCL (supervised) | [email protected] | 46.7 | — | Unverified |
| 8 | LGCL (unsupervised) | [email protected] | 45.3 | — | Unverified |
| 9 | SAD | [email protected] | 45.2 | — | Unverified |
| 10 | AWADA | [email protected] | 44.8 | — | Unverified |
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | FFTAT | Accuracy | 91.4 | — | Unverified |
| 2 | TransAdapter-B | Accuracy | 89.4 | — | Unverified |
| 3 | SAMB | Accuracy | 86.2 | — | Unverified |
| 4 | PDA (CLIP, ViT-B/16) | Accuracy | 85.7 | — | Unverified |
| 5 | SSRT-B | Accuracy | 85.43 | — | Unverified |
| 6 | EUDA | Accuracy | 84.9 | — | Unverified |
| 7 | ProDe | Accuracy | 84.5 | — | Unverified |
| 8 | ECB (CNN) | Accuracy | 81.2 | — | Unverified |
| 9 | CDTrans | Accuracy | 80.5 | — | Unverified |
| 10 | JAN [cite:ICML17JAN] | Accuracy | 76.8 | — | Unverified |
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | ResNet50 (baseline), BatchNorm Adaptation, 8 samples | mean Corruption Error (mCE) | 65 | — | Unverified |
| 2 | ResNet50 (baseline), BatchNorm Adaptation, full adaptation | mean Corruption Error (mCE) | 62.2 | — | Unverified |
| 3 | ResNet50 + ENT | mean Corruption Error (mCE) | 51.6 | — | Unverified |
| 4 | ResNet50 + RPL | mean Corruption Error (mCE) | 50.5 | — | Unverified |
| 5 | ResNet50+DeepAug+AugMix, BatchNorm Adaptation, 8 samples | mean Corruption Error (mCE) | 48.4 | — | Unverified |
| 6 | ResNet50+DeepAug+AugMix, BatchNorm Adaptation, full adaptation | mean Corruption Error (mCE) | 45.4 | — | Unverified |
| 7 | ResNeXt101 32x8d + ENT | mean Corruption Error (mCE) | 44.3 | — | Unverified |
| 8 | ResNeXt101 32x8d + RPL | mean Corruption Error (mCE) | 43.2 | — | Unverified |
| 9 | ResNeXt101 32x8d + IG-3.5B + RPL | mean Corruption Error (mCE) | 40.9 | — | Unverified |
| 10 | ResNeXt101 32x8d + IG-3.5B + ENT | mean Corruption Error (mCE) | 40.8 | — | Unverified |
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | MIC+CSI | mIoU (13 classes) | 75.9 | — | Unverified |
| 2 | DCF | mIoU (13 classes) | 75.9 | — | Unverified |
| 3 | DIDA | mIoU (13 classes) | 70.1 | — | Unverified |
| 4 | Sepico + HIAST | mIoU (13 classes) | 68.1 | — | Unverified |
| 5 | CLUDA+HRDA | mIoU | 67.2 | — | Unverified |
| 6 | SePiCo (DeepLabv2 ResNet-101) | mIoU (13 classes) | 66.5 | — | Unverified |
| 7 | G2L | mIoU (13 classes) | 64.4 | — | Unverified |
| 8 | DAFormer+CSI | mIoU | 61.4 | — | Unverified |
| 9 | FAFS | mIoU (13 classes) | 61.4 | — | Unverified |
| 10 | AdaptSeg + HIAST | mIoU (13 classes) | 60.3 | — | Unverified |
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | CORE-ReID V2 | mAP | 49.5 | — | Unverified |
| 2 | MATNet+DMDU | mAP | 49.25 | — | Unverified |
| 3 | MGR-GCL | mAP | 48.73 | — | Unverified |
| 4 | PLM | mAP | 47.37 | — | Unverified |
| 5 | CSP+FCD | mAP | 45.6 | — | Unverified |
| 6 | PAL | mAP | 42.04 | — | Unverified |
| 7 | CORE-ReID V2 Tiny | mAP | 40.17 | — | Unverified |
| 8 | SPCL | mAP | 38.9 | — | Unverified |
| 9 | UDAR | mAP | 35.8 | — | Unverified |
| 10 | MMT | mAP | 35.3 | — | Unverified |
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | ALDI++ | [email protected] | 77.8 | — | Unverified |
| 2 | ALDI-YOLO | [email protected] | 75 | — | Unverified |
| 3 | MIC(ALDI frame) | [email protected] | 73.1 | — | Unverified |
| 4 | AT(ALDI frame) | [email protected] | 72 | — | Unverified |
| 5 | SADA(ALDI frame) | [email protected] | 71.8 | — | Unverified |
| 6 | PT(ALDI frame) | [email protected] | 70.6 | — | Unverified |
| 7 | RT-DATR(real-time, 640x640) | [email protected] | 67.2 | — | Unverified |
| 8 | DDT | [email protected] | 64 | — | Unverified |
| 9 | MRT | [email protected] | 62 | — | Unverified |
| 10 | MILA | [email protected] | 57.4 | — | Unverified |