Re-labeling ImageNet: from Single to Multi-Labels, from Global to Localized Labels

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Metadata

• Author: Sangdoo Yun, Seong Joon Oh, +3, Sanghyuk Chun
• Organization: NAVER AI LAB
• Paper: https://arxiv.org/abs/2101.05022
• Code: https://github.com/naver-ai/relabel_imagenet

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Highlights

• Motivation. ImageNet label is noisy: An image may contain multiple objects but is annotated with image-level single class label.

• Intuition. A model trained with the single-label cross-entropy loss tends to predict multi-label outputs when training label is noisy.

• Relabel. They propose to use a strong image classifier that trained on extra data (super-ImageNet scale, JFT-300M, InstagramNet-1B) + fine-tuned on ImageNet, to generate multi-labels for ImageNet images. Obtain pixel-wise multi-label predictions before the final global pooling layer (offline preprocessing once).
  

• Novel training scheme -- LabelPooling. Given a random crop during training, pool multi-labels and their corresponding probability scores from the crop region of the relabeled image.
  
  

• Results. Trained on relabeled images with multi-and-localized labels can obtains 78.9% accuracy with ResNet-50 (+1.4% improvement over baseline trained with original labels), and can be boosted to 80.2% with CutMix, new SoTA on ImageNet of ResNet-50.

Related work: Better evaluation protocol for ImageNet

• ImageNetV2: Do ImageNet Classifiers Generalize to ImageNet?
• Are we done with imagenet?
• Evaluating machine accuracy on imagenet. ICML 2020.
• (Not on ImageNet but related) Evaluating Weakly Supervised Object Localization Methods Right. CVPR 2020

The above works have identified 3 categories for the erroneous single labels

1. An image contains multiple objects
2. Exists multiple labels that are synonymous or hierarchically including the other
3. Inherent ambiguity in an image makes multiple labels plausible.

Difference from this work

1. This work also refines training set while previous work only refine validation set.
2. This work correct labels while previous work remove erroneous labels.

Related work: Distillation (I hand-picked some by their practical usefulness in my opinion)

• [Ensemble distillation] Knowledge distillation by on-the-fly native ensemble. NIPS 2018.
• [Self-distillation] Snapshot distillation: Teacher-student optimization in one generation. CVPR 2019.
• [Self-distillation] Self-training with noisy student improves imagenet classification. CVPR 2020.

Difference from this work

1. Previous work did not consider a strong, SoTA network as a teacher.
2. Distillation approach requires forwarding teacher on-the-fly, leading to heavy computation.

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Relabeling Details

Network architecture modification for generating label map

• Original: Feature map [H, W, d] => Global Pooling [1, 1, d] => Predicted label [1, 1, C] with FC.
• Modified: Feature map [H, W, d] => Predicted label map [H, W, C] with 1x1 Conv.
• FC and 1x1 Conv is identical.
• Use EfficientNet-L2, input size 475x475, training image is resized to 475x475 without cropping.
• Label map's spatial size [H, W] = [15, 15], d=5504, C=Top-5 predictions among 1000 (storing 15x15x1000 is expensive)

Generating label map using different architectures

They tried diverse architectures:

1. SoTA EfficientNet-{B1,B3,B5,B7,B8}
2. EfficientNet-L2 trained with JFT-300M
3. ResNeXT-101_32x{32d, 48d} trained with InstagramNet-1B
   And train ResNet-50 with the above label maps from diverse classifiers. Finally label map generated from EfficientNet-L2 is chosen due to its best quality for obtaining the final best accuracy. (Can we ensemble these label maps?)

Important Findings

• When the teacher is not sufficiently strong, the performance will not be better than baseline.
• Local multi-label > global multi-label > local single-label > global single label (original one).
• Combining with original label decreases performance, but yet better than only original label.
• Evaluating on new multi-label ImageNet benchmark, the performance gain is larger.
• Knowledge distillation can still get comparable or better performance but training time is longer.
• Label smoothing on original label obtains surprisingly strong performance, since label smoothing can be as a kind of knowledge distillation.
• Why label map is 15x15? Besides saving storage, I think there's a paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale related to this design choice.

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Training with LabelPooling

Once label map is pre-computed, we can train a new network by the following procedure:

1. Load image & label map (15x15xC)
2. Augmented image = Random crop image (with a bounding box [x, y, w, h]) and resize to (224x224)
3. New target = ROIAlign(label map, bounding box) [h, w, C] + global pooling [1, 1, C] + softmax
4. Train model with <Augmented image, New target> with cross-entropy loss

Discussion on Design Choices

1. Isn't 15x15 label map too small? Due to expensive storage consumption for ImageNet.
2. Why not use knowledge distillation? Due to expensive training time for ImageNet.
3. Can new network also be trained with local labels instead of global ones (same as FCN in relabeling)?