Explanation-based Data Augmentation for Image Classification

1 Introduction . Machine learning models learn decision boundaries based on the training dataset . Ideally , a training dataset should provide sufficient variations in each class so that the model can learn the correct decision boundaries . However , when there are under-represented regions in the training dataset , the model will learn sub-optimal decision boundaries , leading to misclassification of data points [ 1 , 2 ] . One way to address this problem is to augment the training dataset . A common data augmentation approach is to apply different transformations to the training samples [ 3 ] or to mix existing samples [ 4 , 5 ] . However , such data augmentations only explore the neighborhood of these samples and may not cover the under-represented regions . Another approach is to use annotations such as part landmarks to obtain images from image resources such as Flickr or Google [ 6 ] . This approach may introduce out-of-distribution images and degrade the classifier performance . Identifying the appropriate samples to add to the training dataset is crucial . Here , we propose to use model decision explanations to facilitate the search for samples from image repositories and augment the training dataset with samples from the under-represented regions to enable the model to learn the correct decision boundaries . Figure 1a shows the image of a juvenile Black Tern that has been misclassified as White Breasted Nuthatch . A closer examination of the training dataset reveals that it contains very few images of juvenile Black Terns ( 5 out of 30 images ) . These juvenile birds have white heads with black skullcap and black earmark , unlike the adult Black Terns [ 7 ] ( see Figure 1b ) . In fact , the juvenile Black Tern in Figure 1a is more similar to White Breasted Nuthatch as shown in Figure 1c , leading to the misclassification . Clearly , there is a need to augment the training data to include more images of juvenile Black Tern in order to learn the correct decision boundary between Black Terns and White Breasted Nuthatch . Methods to explain model decisions include pixel-level attributions [ 8 , 9 , 10 ] , and high-level semantic concepts such as coarse-grained saliency maps [ 11 , 12 ] , interpretable basis decomposition [ 13 ] , linguistic explanations [ 14 , 15 ] and prototypes [ 16 ] . The work in [ 17 ] designs a Comprehensive 35th Conference on Neural Information Processing Systems ( NeurIPS 2021 ) . Convolutional Neural Network called CCNN that explains the model decision in terms of word phrases , e.g. , the bird in Figure 1a is classified as White Breasted Nuthatch due to the concepts ’ white breast ’ , ’ white belly ’ , ’ long beak ’ . This paper introduces a framework called BRACE ( BetteR Accuracy from Concept-based Explanation ) , which utilizes concept-based explanations to identify candidate samples from image repositories for data augmentation . The key idea is to identify concepts that have led to misclassifications and augment the dataset with images that contain these concepts . We design a utility function that computes the degree of match between the visual features in an image and the set of concepts in the under-represented regions . The proposed approach enables us to select samples with high utility scores and increase the representativeness of the training dataset . Experiment results on multiple datasets demonstrate that BRACE outperforms state-of-the-art augmentation methods . 2 Related Work . Data augmentation is a widely used pre-processing step in training deep neural networks . The objective is to inflate the training dataset with label preserving small transformations to avoid overfitting . Techniques include geometric transformations , color space augmentations , kernel filters , mixing images , feature space augmentation , adversarial training , generative adversarial networks , neural style transfer , and meta-learning [ 3 ] . However , these augmentation methods only explore the neighborhood of existing images and are not able to add samples from under-represented regions [ 18 ] . The work in [ 6 ] taps into the large online image repositories to augment the training dataset . Object bounding boxes and part landmarks annotations in the original dataset are used to select relevant and diverse images . Since the images retrieved are typically noisy , [ 19 ] trains two models , to select in-distribution samples and to re-label the noisy images in the selected set . Both these works may not increase the representativeness of the dataset as they do not consider the under-represented regions in the dataset when deciding which samples to add . Existing explanation methods for deep neural network based image classifiers include pixel-level attribution methods and high-level semantic explanation methods . Pixel-level attribution methods [ 8 , 9 , 10 ] assign an importance score to each pixel in the image based on the contribution of that pixel towards the model decision . On the other hand , semantic explanation methods explain model decisions in terms of human-friendly concepts and can be categorized into post-hoc methods versus fully interpretable classifiers . Post-hoc methods include Grad-CAM [ 11 ] , FLEX [ 15 ] , IBD [ 13 ] , TCAV [ 20 ] , and ACE [ 21 ] . The first three approaches are image-level explanation mechanisms explaining the model ’ s prediction of the given input . In contrast , TCAV and ACE are class-level explanation mechanisms that explain the characteristics of the entire class . Fully interpretable classifiers such as ProtoPNet [ 16 ] and CCNN [ 17 ] explain model decisions in terms of prototypical parts and word phrases , respectively . The proposed BRACE framework is able to utilize explanations from both post-hoc methods and fully interpretable classifiers to obtain new samples for data augmentation . 3 Proposed Framework . Machine learning models often make wrong classifications for classes that have insufficient variance in training data . Adding more diverse samples to the mix will enable the model to improve its accuracy . Intuitively , we want to give more training samples to classes with higher misclassifications so that the model is able to learn the real-world variations . Let C be the set of class labels , and D = { ( xi , yi ) } be the original training set , where yi ∈ C is the class label of image xi . For each class c ∈ C , we want to augment D with a set of new samples that is proportional to the number of misclassifications for that class . Given a deep neural network model M , we compute the ratio of misclassifications rc for class c on the validation set Dv as follows : rc = 1− ∑ ( x , c ) ∈Dv I ( M ( x ) = c ) m ( 1 ) where m is the number of validation samples with class label c , and I is a function that returns 1 when its argument is true and 0 otherwise . We will augmentD with |Dc|×rc number of new samples where Dc ⊂ D is the set of images with class label c. Let Xc be the set of samples obtained using class c as the keyword query on image repositories . This set is typically noisy , which may degrade model performance . As such , we will assess whether the new image is in the under-represented region and whether it contains concepts that led to the misclassification . We say that x is an under-represented sample if it has visual features of class c , but the model ’ s confidence that x belongs to a different class c̄ 6= c is high . We compute the under-representation score as follows : β ( x , c , c̄ ) = fx.fc ‖fx‖‖fc‖ × eP ( c̄|x ) ( 2 ) where fx is the feature vector of x and fc is the average feature vector of all the images in class c , P ( c̄|x ) is the predicted probability of x belonging to class c̄ . Feature vectors are extracted from model M . A high β value indicates that x is similar to some images in class c , but the model has classified it as class c̄ . Such samples are more informative , and using them for training may help M generalize better . Further , the sample x should not only possess features of class c but should also contain specific concepts that caused the model M to misclassify it as class c̄ . Adding images with these specific concepts will help the model to better differentiate class c from class c̄ . Let Sc→c̄ be the set of concepts that caused M to misclassify images of class c into c̄ , and C̄ = ⋃ c̄ . Let ∆ ( Sc→c̄ , x ) be the function that computes the degree of match between the visual features in an image x and the concepts in Sc→c̄ . The derivation of Sc→c̄ and ∆ ( . ) differ depending on the explanation mechanism used ( see Section 4 ) . The utility score of a sample x is computed as follows : utility ( x ) = ∑ c̄∈C̄ [ β ( x , c , c̄ ) ×∆ ( Sc→c̄ , x ) ) ] ( 3 ) Then |Dc| × rc samples with the highest utility scores are added to the original train dataset . We repeat this process for all the classes in D. We fine-tune the weights of M with the augmented training dataset . Note that adding new samples may result in data imbalance , and this is resolved by using weighted cross-entropy loss during fine-tuning where the weight of class c is given by 1 |Dc| ( 1+rc ) . With this , there will be minimal change to the weights of the classes where M already performs well . Algorithm 1 gives the details of the BRACE framework . 4 Derivation of Sc→c̄ and ∆ ( . ) . In this section , we describe how we derive the concepts that cause the misclassification , Sc→c̄ , for two popular explanation mechanisms : the fully interpretable classifier with linguistic explanation and the black-box classifier with post-hoc explanation using heat maps . We also show how to compute the degree of match ∆ ( . ) in these cases . 4.1 Fully Interpretable Classifier with Linguistic Explanation . The work in [ 17 ] introduces an additional concept layer to the CNN-based architecture to guide the learning of the associations between visual features and word phrases extracted from image descriptions . This Comprehensible Convolutional Neural Network ( CCNN ) explains its decisions in word phrases corresponding to different visual concepts . The training objective function considers concept uniqueness which encourages each learned concept to correspond to only one word phrase , and mapping consistency which aims to preserve the distance between the learned concept and its corresponding word phrase in a joint embedding space . Together with classification accuracy loss , CCNN is currently the state-of-the-art fully interpretable classifier . Here , we discuss how the proposed BRACE framework can further improve CCNN classification accuracy . Algorithm 1 : BRACE input : Classification model M , Original training dataset D , Set of class labels C output : Fine-tuned weights θ θ ←Weight initialization. . Initialize θ with pre-trained weights of M for iter ∈ [ 0 , max_iterations ] do D′ ← D for c ∈ C do rc ← Calculate miss-classification ratio as in Eq . 1 . Xc ← Obtain images given the class name of c as the query term . for x ∈ Xc do for c̄ ∈ C̄ do β ( x , c , c̄ ) ← Calculate under-representation score as in Eq . 2 . Sc→c̄ ← Derive concepts caused misclassifications . ∆ ( Sc→c̄ , x ) ← Calculate availability of concepts Sc→c̄ . end utility ( x ) = ∑ c̄∈C̄ [ β ( x , c , c̄ ) ×∆ ( Sc→c̄ , x ) ) ] end D′ ← D′ ⋃ { |Dc| × rc samples with the highest utility scores } end θ ← finetune_weights ( M , D′ ) end Let V = { v1 , ... , vn } be the set of visual features extracted by the concept layer of CCNN for an image x . Each vi ∈ V corresponds to a concept described by a word phrase p ∈ P = { p1 , ... , pn } . CCNN employs a Global Average Pooling ( GAP ) layer to express its classification decision as a weighted sum of the learned concepts . Suppose the output of the GAP layer is { o1 , ... , on } where oi corresponds to concept i in x . Then the contribution of i to the class decision c is given by oi × wi , c ∈W , where W ∈ Rn×C is the weight matrix of the fully connected layer in CCNN . We apply the softmax function to obtain the percentage contributions and select the set of concepts whose contribution is greater than some threshold . We will take the top-5 if there are more than five concepts that satisfy the criteria . Here , we set the threshold to be 1n . For instance , CCNN classifies the bird in Figure 2 as Nashville Warbler and explains that the concepts `` Yellow breast '' , `` Grey crown '' and `` White eyering '' accounted for 47 % , 19 % , and 14 % of the decision , respectively . Given Xc→c̄ , the set of images with class label c that have been misclassified as c̄ , we compute the number of images in Xc→c̄ that contains the concept i . The top k concepts with the highest number form the set Sc→c̄ . We set k = 3 in our experiments . Recall , each visual feature vi in CCNN concept layer is mapped to a word phrase pi describing a concept , and vi is activated only when the concept occurs in the image . As such , given the set of concepts Sc→c̄ , we utilize their corresponding visual feature activations to determine the degree of match of a concept i in an image x as follows : αi = − log [ 1− 1 1 + e−ōi + ] ( 4 ) where ōi is the normalized oi obtained by subtracting the mean activation of the GAP layer . The negative log function gives more weight to an image with one highly activated concept than one with multiple weakly activated concepts . With this , the degree of match between the set Sc→c̄ and image x is given by : ∆ ( Sc→c̄ , x ) = k∑ i=1 αi ( 5 )

Summary: The paper proposes to use interpretation to select effective instances for augmenting training data. A utility function is developed, where images with high scores mean that: 1) the images are helpful for better learning class-c data, 2) the images could significantly correct the drawbacks of the model M. The paper discusses two interpretation scenarios, interpretable models (GAP+linear model) and post-hoc interpretation. The experiment includes a comparison to two types of baseline methods, data augmentation methods and data selection methods. Ablation studies are also conducted to show the effectiveness of the two components in the utility function. Different parts in the current version are somehow fragmented.