Prototype antithesis for biological few-shot class-incremental learning
Authors: Binghao Liu
Han Yang
Fang Wan
Fei Gu
Venue: ICLR 2025 Poster (2025)
Keywords: Biological Recognition; Few-Shot Learning; Class-Incremental Learning; Prototype Antithesis
Primary area: transfer learning, meta learning, and lifelong learning

Abstract: Deep learning has become essential in the biological species recognition task. However, a significant challenge is the ability to continuously learn new or mutated species with limited annotated samples. Since species within the same family typically share similar traits, distinguishing between new and existing (old) species during incremental learning often faces the issue of species confusion. This can result in "catastrophic forgetting" of old species and poor learning of new ones. To address this issue, we propose a Prototype Antithesis (PA) method, which leverages the hierarchical structures in biological taxa to reduce confusion between new and old species. PA operates in two steps: Residual Prototype Learning (RPL) and Residual Prototype Mixing (RPM). RPL enables the model to learn unique prototypes for each species alongside residual prototypes representing shared traits within families. RPM generates synthetic samples by blending features of new species with residual prototypes of old species, encouraging the model to focus on species-unique traits and minimize species confusion. By integrating RPL and RPM, the proposed PA method mitigates "catastrophic forgetting" while improving generalization to new species. Extensive experiments on CUB200, PlantVillage, and Tree-of-Life datasets demonstrate that PA significantly reduces inter-species confusion and achieves state-of-the-art performance, highlighting its potential for deep learning in biological data analysis.

Published as a conference paper at ICLR 2025
PROTOTYPE ANTITHESIS FOR BIOLOGICAL FEW-SHOT CLASS-INCREMENTI LEARNING
Binghao Liu Han Yang' Fang Fei Gu 'DAMO Academy, Alibaba Group, Hangzhou, 310023, China
"Hupan Lab, Hangzhou, 310023, China UUniversity of Chinese Academy of Sciences, Beijing, 101408, China
ABSTRACT
Deep learning has become essential in the biological species recognition task. However, significant challenge is the ability to continuously learn new mu- tated species with limited annotated samples. Since species within the same fam- ily typically share similar traits, distinguishing between new and existing (old) species during incremental learning often faces the issue of species confusion. This can result in "catastrophic forgetting" of old species and poor learning of new ones. To address this issue, we propose a Prototype Antithesis (PA) method, which leverages the hierarchical structures in biological taxa to reduce confu- sion between new and old species. PA operates in two steps: Residual Prototype Learning (RPL) and Residual Prototype Mixing (RPM). RPL enables the model to learn unique prototypes for each species alongside residual prototypes repre- senting shared traits within families. RPM generates synthetic samples by blend- ing features of new species with residual prototypes of old species, encouraging the model to focus on sereciss-uiquee traits and minimize species confusion. By integrating RPL. and RPM, the proposed PA method mitigates "catastrophic for- getting" while improving generalization to new species. Extensive experiments on CUB200, PlantVillage, and Tree-of-Life datasets demonstrate that PA signifi- cantly reduces inter-species confusion and achieves kate-of-theean performance, highlighting its potential for deep learning biological data analysis.
INTRODUCTION
With advances in data science and computing, computer techniques are crucial in fields like biology, offering solutions for species recognition and evolutionary analysis. However, limited data on newly discovered or mutated species poses a challenge, as data annotation requires expertise and is costly. Additionally, similar traits among species within the same family complicate recognition. These issues-de data scarcity and species similarity- can cause confusion between new and existing species during continual learning, leading to poor performance
species and forgetting of old ones.
To overcome this, we introduce the few-shot innsemnncrmmetlll learning paradigm to biological re- search, allowing models to be trained on existing data and updated with only few samples from new species. This approach enables continuous learning from new species while preserving knowledge of previously learned ones, addressing data limitations, and improving scalable species recognition. It underscores the vital role of advanced deep learning in advancing biological discovery.
Few-shot class-incremental learning (FSCIL) typically faces two key challenges: "catastrophic for- getting" and "over-fitting". These issues arise when models must learn new classes with few sam- ples and no access previous data, Early methods address forgetting through knowledge distil- lation, where the old model's outputs guide the new model. However, when training samples for new classes are limited, the model becomes more prone to over-fitting. Recent approaches /Zhan et al. (2021): Zhou et al. (2022) mitigate forgetting by freezing the feature extractor and selectively updating classifier weights, preserving old class performance. However, in biological contexts, dis- tinguishing between new and previously learned species presents an additional challenge due to their
Coorrsponndnnce Fang Wan Branfang@ucas.ac.oo Binghao Liu (liubinghao. Ibh @alibaba-ine.com)

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Published as a conference paper at ICLR 2025
Base session
Session
Data stream
feature samples
confusion
species prototype
Prior works
prototype before PA
residual
residual prototype mixing
prototype karning
(ours)
residual prototype
Feature Space
Figure 1: Comparison of prior works and the Prototype Antithesis (PA) method. In incremental learning, when similar new species resemble existing ones, their prototypes tend to be close, leading to confusion during classification (upper). In contrast, our proposed PA addresses this issue by de- composing the prototypes into two parts: the unique prototype specific species and the residual prototype shared within its family. The residual prototype is employed in the residual prototype mixing for new species learning, which maximizes the distance between similar species prototypes, thereby reducing classification confusion. (Best viewed in color)
close evolutionary relationships. The similar traits of species within the same family make it difficult for classifiers to differentiate between them. This complicates learning new classes and increases inter-species confusion, reducing performance on previously learned species (Fig. [ prior works).
In this study, propose Prototype Antithesis (PA) method to address these challenges. PA en- courages the model to learn both the most discriminative prototypes unique to individual species and the residual prototypes shared by species within the same family. During incremental learning, PA generates new feature samples for model learning by combining residual prototypes from pre- viously learned species with features from new species. This process not only helps the classifier better distinguish between closely related new and old species, but also mitigates over-fitting caused by the limited data available for new species, Fig. (PA).
The PA method operates in two key steps: Residual Prototype Learning (RPL) and Residual Pro- totype Mixing (RPM). RPL conducted during base training and can be further divided into two phases. In phase one, images are assigned with "species-level" labels, and those images are used for classification training by calculating the cosine distance between the features and set of randomly initialized prototypes. After training, those prototypes converge represent the most discrimina- tive semantics the corresponding species. In phase two, the ppecies-unique and family-shane semantics are decoupled by bi-directional optimization process. Specifically, we first compute the residuals between the features and their corresponding prototypes. Then these residual features are assigned with "family-level" labels for training, while the original features are classified by species labels. This bi-directional classification procedure ensures the convergence of species-unique and family-shared traits into their respective prototypes, referred (residual) prototypes. RPM combines the residual prototypes of previously learned species with the new species features using a feature-mixing strategy to generate synthetic feature samples. These synthetic feature samples are labeled as their corresponding new species and are used to facilitate model learning. To classify these synthetic features accurately, the model must capture distinctive traits that differentiate them from residual prototypes and previously learned species, resulting in a well-separated decision boundary. Additionally, the synthetic features act as augmented data, in- creasing the number of training samples and helping to reduce over- fitting on the prehensive experiments conducted on Tree-of-Life Stevens et al. (2024), as well as on the PlantVil- lage Hughes & Salathé (2013) and CUB200 P. et al. (2010) datasets, demonstrate that approach achieves state-of-the -ott----ttt--tttt- performance.
species prototypes and family
species. Com-
The contributions of this study listed below:
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We propose novel Prototype Antithesis (PA) method to address the challenges of species confusion and over-fitting in few-shot class-incremental learning (FSCIL), particularly within the biological domain.
We introduce Residual Prototype Learning (RPL) and Residual Prototype Mixing (RPM) to enable the model to capture both species-unique and family-shared traits, focusing on distinguishing between similar new and old species while augmenting training samples.
. Our proposed PA method achieves state-of-the-art performance across both computer vi- sion and biological benchmarks, demonstrating its superiority in addressing the unique challenges of FSCIL.
2 RELATED WORKS
Biological Recognition. Biological data, characterized by its hierarchical category structure, is fre- quently used in fine-grained classification tasks, such as those involving the CUB200 dataset P. et al.] (2010). These tasks are particularly difficult because subspecies within the same family often dis- play highly similar appearances, making it challenging for models to differentiate them, especially when new species are constantly being discovered. Moreover, these newly identified species often come with limited samples, hindering the model's ability to learn effectively. While recent work like Bio-CLIP SStevens al. (2024) has introduced foundation models for biological research, struggles with continual learning and tend to misclassify closely related species.
Few-shot Learning. The goal to learn new classes with limited training samples. Existing meth- ods have made significant progress through metric learning, meta-learning, and data augmentation.
Metric learning Vinyals et al. (2016); Snell et al. 20:7p; Sung et al. (2018); 20hagg et al. (2020); Liu
et al. [202I); Yang et al. (202 1a); Liu al. 2021); Li et al. (2021) maps new class features into
representation space learned from base classes with sufficient data. Meta-learning
et al. (2017);
Elsken et al. (2020); Sun et al. (2019) develops optimization strategies to enable models to general-
ize to new classes with limited data. Data augmentation Zhang et al.] (2019): Li et al. (2000) Kim et al. (2020); Yang al. (2021b) generates synthetic samples reduce overfitting and support new class learning. However, these methods mainly focus on learning new classes and often overlook the problem of forgetting old classes during incremental learning. GFSL (Scooneeld al. (2019) addresses this by aligning old and new class distributions to mitigate forgetting, but it struggles with large distribution gaps and risks class confusion when old and new classes are highly similar.
Incremental Learning. Incremental learning can be divided into task-incremental and class- incremental learning, with the key difference being that task issseincremettll learning requires task IDs, while class -ntrremetteee does not Masana et al. (2020). Existing methods fall into three categories: memory replay, model regularization, and architecture configuration methods. Memory replay Re- buffi et al. 2017);; Chaudhry et al. (2018) methods store old class samples to prevent forgetting. Model reg regularization Li & Hoiem| (2018); Dhar et al. (2019) methods add terms to the loss func- tion to reduce forgetting. Architecture configuration Serrà et al. (201 8); Mallya & Lazebnik (2018) methods use techniques like model pruning to adjust the feature space. However, these approaches still face over-fitting issues, particularly with limited training samples for new classes
Few-shot Dlass-nncremental Learning, Under few-shot class-incremental learning (FSCIL) set- tings, the model is first trained on base classes with sufficient data and then required to generalize to new classes with only a few samples. The primary challenges of FSCIL include not only "catas- trophic forgetting" but also the issue of "over-fitting" due limited training data. Feature topology preservation methods TTo et al. (2020); Zhang et al. (202T) maintain the feature topology of old classes using techniques like neural gas or decoupled training strategics to prevent forgetting. Net- work regularization methods Akyürek et al.) (202T); Kang et al. (2022) align the weight vectors of new classes with those of old classes or selectively update model weights to learn new classes with- out degrading the performance of old classes. Data generation methods Cheraghian et al. (2021); Liu et al. (2023) generate synthetic feature samples to alleviate over-fitting during incremental learn- ing. However, the confusion between new and old classes remains an unsolved problem in FSCIL. Since the model can only access data from individual incremental sessions, it struggles to learn well-separated decision boundaries between old and new classes, especially when these classes similar. This confusion can lead to both forgetting of old classes and poor learning of new ones, which is the problem this study aims to address.
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Residual Prototype Learning
Residial Prototype Mixing
Base specles data data X
species
Features
Predicion
Q
1D
Procotypes 9.
Protutyppcc O
y
Predictio
Mix
(())
Pooch Plorar
Rosidhaal protetgpe
4ysh Leef
Resdual
Residial Foomoss R
Festure mit
00
funetioo
Phose two only
Festure cxtrecter
Figure 2: The proposed Prototype Antithesis (PA) method consists of two components: RPL and RPM. RPL has two phases: phase one, images are encoded by the feature extractor 0, and clas- sified using "species-level" labels; in phase two, residuals between the base (old) species features and species prototypes are calculated and classified using "family-level" labels, whereas the origi- nal features continue to be classified by "species-level" labels. In RPM, these residual prototypes are mixed with new species features to generate pseudo-samples, optimizing species prototypes and refining the decision boundary. (Best viewed in color)
PRELIMINARY
Few-shot class- maas-incrementl learning can be broadly divided into two stages: the base training stage and the incremental learning stage. During the base training stage, the model is trained using base classes ((º), which have sufficient training data. the incremental learning stage, the new classes are divided into T groups, denoted as C(),, where tal sessions. In the 2h incremental session, only the dataset D for classes C(+) is available for model learning. However, during evaluation, the model is required to classify both old and new classes from the combined set {C(0) ..... c(O). Importantly, for any i j i2, we have C(is) ((((2)
.... T, corresponding the T incremen-
Ø, meaning that the classes in different incremental sessions mutually exclusive. This setup imposes the challenge that the model must learn new classes without forgetting previously learned ones, despite
having access to the data from older classes during the incremental sessions.
METHODOLOGY
In this section, we introduce the proposed PA method. First, we outline the flowchart of the base training and incremental learning procedures. Next, we provide detailed explanations of the resid- ual prototype learning and residual prototype mixing techniques. Finally, we present a theoretical analysis of the proposed prototype antithesis method.
4.1 OVERVIEW
The structure of the proposed PA method illustrated in Fig. The model consists of fea- ture extractor tr, species prototypes O_ {May M -e J. and residual prototypes 0, (Pro Mri .r...... }. We employ ResNet-18 as the feature extractor, which encodes images into feature vectors. The prototypes, 0. and O,,, act as classifiers, using cosine distance between the features and prototypes for classification. During base training, the feature extractor and prototypes are optimized using the residual prototype learning strategy. In the incremental learning phase, the species prototypes are further refined through residual prototype mixing, ensuring a well-separated decision boundary between new and existing species. During the inference stage, the features of both old and new species are classified solely by the prototypes, without incorporating the residual.
4.2 RESIDUAL PROTOTYPE LEARNING
In standard classification tasks, most existing methods focus on learning the most discriminative representations, allowing classifiers to establish well-separated decision boundaries when all classes
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belong to a closed set. However, in few-shot class- incremeanlt learning (FSCIL), where new classes are introduced incrementally, previously learned decision boundaries may struggle to distinguish be- tween and old classes. For example, when new and old species belong to the same family (c.g., apple leaf and peach leaf), the classifier may confuse them due to their shared "leaf" characteristics.
To address this issue, the model needs to differentiate between "shared" and "unique" traits of species, which is the goal the proposed Residual Prototype Learning (RPL), Fig. (left). RPL. operates in two phases, in phase one, given training samples ( from the base species, the feature extractor 0, and species prototypes Oe are jointly trained to classify the base species. The training objective for phase one is formulated as follows:
arg min C(Y (0) 0,, 0.) 0r.0c s.t. Y cos(F (
F(O) = f(x(0);
where C is the classification loss, represents the feature extraction process, cos calculates the cosine similarity between the feature and each prototype individually, resulting in set of scores, and Y are the "species-level" labels. After training, the learned species prototypes capture the most discriminative features of the base species. In phase two, for any base species given the features F(O) and the species prototype He, (where Ha, E 02), the residual features are computed as R, = F(O) Me: Here, R, contains both secondary discriminative features and background features, with the secondary discriminative features likely representing traits shared across the family. To decouple "family-level" traits from background noise and the species prototypes (since the most discriminative features might also include some "family-level" characteristics), we assign "family- level" labels to the residual features and classify them by calculating the cosine distance between
the residual features and the prototypes. Mcanwhile, the original features F!!) are classified based on their "species-level" labels. The optimization objective for base species in phase two is as:
arg min [(Y; Y;: 0r. 0o) + C(Y ( θr. 0.) 0,
s.t. Y = cos R,; Or, 0,),
(0) cos cos(F 0,, 0c, 0,),
where Y and Y!!) denote the "family-level" and "species-level" labels, respectively. The bi- directional optimization process defined in Eq. 2 ensures that the residual prototypes 0, converge towards "family-level" traits, while simultaneously driving the species prototypes 0, to focus "species-unique" traits. These prototypes are jointly optimized to refine the decision boundary, ef- fectively distinguishing between traits that are unique species and those shared across the family. Please note that if a single species is restricted to a specific environment during base training, envi- ronmental features might be misinterpreted species-specific traits. This issue can be resolved by enhancing the diversity of the data used during the base training phase.
4.3 RESIDUAL PROTOTYPE MIXING
Inspired by human's 's reference- learning Kriegeskorte & Douglas (0018), we propose Resid- ual Prototype Mixing (RPM), which drives the model to learn new species by referencing existing knowledge, Fig. 2 (right). The residual prototypes, optimized by Eq, 2 represent family-shared traits likely to appear in closely related species during new species learning. To address this poten- tial confusion, we mix the residual prototypes of old species with new species features to encourage the model to capture the unique characteristics of the new species.
Specifically, in the hh incremental session, given the residual prototypes 0, and features F(t), the features of the 1tth new species are represented as F(!)) where this species belongs to the 7th family. We generate synthetic feature samples as ( = F Mry. and these synthetic feature samples are assigned their original "species-level" labels for classification, with the optimization process for the th species formulated as:
arg min C (xx!), 8( e..
e.
(3)
{(11
k = cos (5 0z, 0,
s.t.
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where A denotes the "species-level" labels. To explicitly explain the optimization process of Eq. we decompose the new species features into species-unique and the other (family-shared ko where F and F are orthogonal, meaning they represent non-overlapping features. Consequently, the synthetic features are expressed as: S +Mr,) (F ko + Mr,) + km. = F' + F enhanced with family-shared traits. For the new species prototypes, the limited training data makes challenging for the model to learn accurate prototypes. Consequently, these prototypes often contain coarse semantic information, combining both the most and second most discriminative features, which are decomposed into family-shared (residual) and species-unique components; Mox Mr; +
and background) features: F() =
kuu + F
FOO ko = ko + Mr, represents the other features
ku where
k Mace and pr; are calculated as: Mega- Thus, the cosine similarity between (
(F
+ +F()) ku
(Hrry + Mavu)
(F ko + F
Mry
(0) Mox) (ozz)
(1, (r,)) cos(S))
(4)
(()
|| II
(t) Mrg
To avoid misclassifying k as the jth family label, the difference term of Eq. A cos
cos( (ex,) - cos(S) l (ry)) must be maximized, As cosine similarity depends only on the angle
between vectors, assume Mex and Mr; unit vectors, and the difference term simplifies to
F Mr," Since only MoLu is the optimization target, maximizing A cos equals maximiz-
A cos-
ing F ku Megu. which enhances the aligmment between the unique features of the new species and its prototype, improving the model's ability focus on distinctive features. Furthermore, we mix new species features with all previously learned residual prototypes to generate additional synthetic features. The features mixed with unrelated family (residual) prototypes are then used as augmented training data to help reduce model over-fitting.
4.4 THEORETICAL ANALYSIS
We analyze Prototype Antithesis (PA) from the perspective of prototype independence and sample concentration. Given prototypes O. {Mag Me M422 ... including both old and new species,
define the class separability (CS) as:
Me
Mec 12
CS ΣN (1 cos(fm: Mc,)) + E (1 cos (oo))
(5)
where i and denote any two different species, while f. and f. represent their feature samples. In this formulation, the numerator measures the independence or separation of the species prototypes He, and Mey (unit vectors). while the denominator reflects the concentration of samples around their respective species prototype, quantifying how well the samples align with their class center. The goal of a classification model is to maximize class separability, where the large separation between species prototypes and tight concentration samples around their respective prototypes leads to better classification performance. Without loss of generality, let us assume that the prototypes of an old species μe; and a new species Pe, are related as Me; = Pe. + 8, || || E [o, 1). where p.e, represents the similar parts with Me., and 8 is orthogonal to Me, and j.c.. representing the unique parts. Substituting this into Equation 5 we can rewrite the class separability a
1 1 Me, (2 - (,c,))
CS
(6)
Σm= (1 cos(fm:: Mc.)) + E. (1 cos(foi (os(f))Me)))
While all methods minimize the denominator using classification loss, the key difference lies in the numerator. The term Pec, (2 (ac)) is monotonically increasing over the interval [0, 1]. In conven- tional approaches, when the new species prototype Mey is similar to the old one Me; Pe. becomes large, indicating prototype overlap and reducing the numerator in Eq. However, PA reduces this overlap by emphasizing species-unique features (0), decreasing fo., thereby increasing the numera- tor in Eq. 6 and improving class separability.
"The proof is included in the "Appendix". Please refer to the "Appendix" for the detailed derivation.

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Table 1: Ablation experiments of the proposed "RPI." and "RPM". The baseline method is standard classification network consisting of a feature extractor and classifier. "Meanotd" and
" "M nan denote the average accuracy of old and new species across all incremental sessions.
Accuracy in each session (%)
Baseline
RPL
RPM
Meanold
M Meannew
PD
2
4
99.47
93.83
90.76
90.47
86.21
95.63
81.58
13.26
99.47
94.21
93.56
92.12
88.86
96.89
84.17
10.61
/
V
99.50
95.83
93.45
93.24
90.51
98.47
86,59
8.99
EXPERIMENTS
We conduct comprehensive experiments on several public datasets for model analysis, and compare proposed method with the state-of-tee oate--of-the-ar the-art methods under FSCIL settings.
5.1 EXPERIMENT SETTINGS
Datasets. We adopt CUB200 P. et al. (2010). PlantVillage Hughes & Salathé (2015), and Tree- of-Life Stevens et al. (022) as benchmarks. In the FSCIL task, classes split into base and incremental categories. Under the N N-way K-shot setting, the incremental classes are divided into T groups, corresponding to the T incremental sessions. In each session, N classes are presented for training, with each class containing K samples. For the CUB200 dataset, 100 classes are used as base classes, and the remaining 100 classes are divided into 10 sessions (T 10), with each session containing 10 classes (10-way) and 5 samples per class (5-shot). In the Plant Village dataset, 19 classes serve base classes, while the remaining 20 classes form incremental classes under 5-way 5-shot setting. For the Tree-of-Life dataset, we randomly sample 100 classes for evaluation, where 60 are base classes, and the remaining 40 are split into groups under the 5-way 5-shot setting.
Model Training. To maintain consistency with prior work, we use a ResNet-18 pretrained on Ima- geNet as the feature extractor for the CUB-200 dataset, while for the Plant and Tree-of-Life datasets, we also use ResNet-18 18 as the backbone. The optimizer is SGD, with a "milestone" learn- ing rate decay strategy. For data pre- rre-processing we apply image normalization, random cropping, random resizing, and horizontal flipping for data augmentation. During base training, we use a batch size of 128 and an initial learning rate of 0.004, training for 100 epochs. In the incremental learning phase, the classifier is optimized over 100 epochs with learning rate of 0.01. We conducted experiments with different seeds on the fixed datasets and reported the averaged results to minimize randomness. All experiments were performed using Pytorch 1.11.0 on Nvidia A800 GPUs.
Evaluation. We adopt "classification accuracy" and "performance drop (PD)" as the evaluation metrics. "Classification accuracy" validates the discriminating ability of the model, and it is defined TP+TN as accuracy #TP+IN+F44E where TP, TN, FP and FN denote the numbers of true positives, true negatives, false positives, and false negatives, respectively. "PD" measures the model forgetting and is defined as PD accuracy(0) accuracy(T), where accuracy(0) and accuracy(T) denote the model performance in the 0-th and T-th sessions, respectively.
5.2 ABLATION STUDY
To rigorously assess the effectiveness of our proposed approach, we perform ablation exper- iments on the PlantVillage dataset.
Table 2: Comparison of "CAM", "Grad-CAM" and the proposed "RPL".
Residual Prototype Learning. As seen in Ta- [ the proposed RPL increases performance on old species by 1.26% and new species by 2.59% over the baseline. This is attributed to the model's 's ability to classify both unique and shared features of the base species, resulting in more compact decision boundary that supports learning in both old and new species.
Method
Meanatd
Meannew
Similarity
CAM
95.31
81.25%
6.53%
Grad-CAM RPL
95.32%
79.82% 86.59
8.15%
98.47% %
1.89 %
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Without Prototype Antithesis (Baseline)
With Prototype Antithesis (Ours)
spot
spoi
apple (new)
cesidal
apple an (new)
apple seab (ohi)
(old)
grape rot (ol))
mist (old)
(ol))
grape spot (neel.
STRO
grape spot (new)
Figure 3: t-SNE visualization feature distributions comparing models with and without Prototype Antithesis (PA). Without PA, the features of new species overlap with those of similar old species. In contrast, with PA, the new species features are pushed away from the residual features, effec- tively distinguishing the new and old species and creating well-separated decision boundary. (Best viewed in color)
Residual Prototype Mixing. Table demonstrates that combining RPL with RPM boosts old and new species performance by 2.84% and 5.01 %, respectively, and decreases "PD" by 4.37%. RPM reduces the overlap of shared components between new and old species, effectively minimizing species confusion. Together, RPL and RPM work synergistically to reduce "old species forgetting" while enhancing "new species generalization".
Feature decomposition strategy. To assess the effectiveness of different feature decomposition strategies, use CAM Zhou al. (2016) and Grad-CAN (aseaaaraaa et al. (2017) to distinguish be- tween unique and common features. Specifically, we set threshold to extract the highest-response regions from the feature maps as unique features, while the sronnary-higg-resppone resent common features. As shown in Table 2 RPL achieves the highest mean accuracy and the lowest similarity between unique and common features, which can be attributed to its bi-directional optimization that more effectively decouples unique features from shared ones.
regions rep-
5.3
MODEL ANALYSIS
In this section, we further conduct comprehensive experiments for detailed statistical results and visualization.
Feature distribution. We employ t- SNE visualize the feature distribu- tions of similar old and new species, as well as the residual feature distri- butions. As depicted in Fig Bleft,, without the proposed prototype an- tithesis, feature samples of the old and new species overlap at the edges of the distribution. However, with the introduction of the prototype an- tithesis, the distance between the new species and the residual features is maximized, making the features of the new and old species more distin- guishable rright), This adjust- ment optimizes the decision bound- ary and reduces species confusion.
Raseline
Tre label
1 A.s Jo &
Predicted label
Predicted libel
Figure Confusion matrix of the baseline method and our proposed PA approach. Compared with the baseline method, PA significantly reduces the false prediction among old and new species, alleviating species confusion.
Confusion Matrix. We plot the con- fusion matrix for both the baseline method and our proposed PA method. As shown in Fig. the PA method significantly reduees false classifications between old and species. These results demonstrate that PA effectively minimizes confusion between new and old species, mitigating for- getting of old species while enhancing learning for new species.
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Table 3: Performance comparison the CUB200 dataset. "*p" indicates results that implemented using the official code.
Method
Accuracy In each session (55)
PD 1
10
TOPIC Tt al.
20201
68.68
62.49
54.81
49.9 99
45.25
38.35
35.36
32. 2.
28.31
26.28
42.40
SPPR ct
57.43
52.68
50.19
46.88
43.07
40.17
63.63
37.33
31.35
SFMS Thrraghaann ot
2021
68.78
59.37
59.32
54.96
52.58
49.81
48.09
46.32
44.33
43.43
43.23
25.55
Mazumder et
2021
72.77
65.51
62.66
61.10
58.
57.78
57.26
54.21
17.38
CEC
ZTanng
75.85
71.94
68.50
63,50
62.43
58.27
57.73
55.81
54.83
53.52
52.28
23.57
Meta:
FSCIL
2022
75.90
72.41
68.78
64.78
62.96
$9.99
58.30
56.85
54.78
53.82
52.64
23.26
FACT
Zhou 0f al. 2012
75.90
73.23
70.84
66.13
65.56
62.15
61,74
59.83
58.41
57.89
56.94
18.96
SOFT
Net Kanz
2025
78.07
74.58
71.37
67.54
65.37
62.60
61.07
59.37
57.53
57.21
56.75
21.32
WaR
Kim
2211-5
77.74
74.15
70.82
66.90
65.01
62.64
61.40
59.86
57.95
57.77
57.01
20.73
BiDistZhap 74725
79.12
74.99
70.87
67.30
65.89
63.45
61.40
60.11
58.61
58.23
57.48
21.64
NC.FSC Yany 2025
80.45
75.98
72.30
70.28
68.17
66.16
64.43
63.25
60.66
60.01
59.44
21.01
OrCo* Ahmed al 2024
74.72
66.06
64.72
62.88
61.89
$9.69
58.95
59.03
57.24
$7.79
57.18
17.54
TEEN Wang
72.81
67.77
64.40
63.25
62.29
60.32
18.13
PAtours]
78.69
75.59
72.71
68.71
68.37
65.77
64.75
63,59
62.76
62.02
61.19
17.50
(a) Performance on PlantVillage
(b) Performance Tree-of-Life
50
4.36
90
45
70
TECKN
40
Accuracy
60
CEC
35
CBC
FLCNN
1.29
50
FACT
FACT
TEEN
40
BIDiS:
25
30
PA (ours)
PA found)
20
20
Session
Session
Figure 5: Performance the PlantVillage and Tree-of-Life datasets.
5.4 COMPARISON WITH THE STATE- -H-E--FFTTEE-AT METHODS
We compare PA against state-of-the-art methods on both computer vision benchmarks (c.g.. CUB200) and biological datasets (e.g., PlantVillage and Tree-of-Life).
CUB200: As shown Table 3 our proposed PA outperforms TEEN and OrCo by 1.88% and 4.01%, as well achieving lower "PD" compared most other methods. Plant Village: From F 5(aa) we can see that PA outperforms other methods with significant margins. The categories in the ViantVillage dataset (different leaves with discase) share many common traits, and the significant improvement in performance shows PA'S superiority in distinguishing closely related old and new species. Tree-of-Life: PA outperforms OrCo Ahmed et al. (2024) and BiDist Zhao al. (2023) by 6.26% and 1.28% on this challenging dataset, respectively, Fig. b)).. This result confirms PA'S robustness in handling high-diversity biological data.
6 CONCLUSION
In this paper, we propose Prototype Antithesis (PA) method address the issue of species con- fusion in biological few-shot class-incremental learning. The PA method comprises Residual Pro- totype Learning (RPL) and Residual Prototype Mixing (RPM). RPL decomposes species-unique and family-shared (residual) prototypes, while RPM generates synthetic samples by blending new species features with residual prototypes. Together, RPL and RPM optimize the decision bound- ary between old and new species, mitigating "old species forgetting" and enhancing "new species learning". PA offers a novel perspective for recognizing biological species and traits using few-shot class-incremental learning.

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Published as a conference paper at ICLR 2025
AUTHOR CONTRIBUTIONS
Fei Gu served the project manager. Binghao Liu proposed the idea of the paper, designed the methodology. conducted the experiments, and wrote the manuscript. Han Yang participated in dis- cussions on experimental settings, Fang Wan participated in idea discussions and provided sugges- tions for figure visualization.
ACKNOWLEDGMENTS
This work was financially supported in part by grants from the Biological Breeding Major Projects (2023ZD04076).
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A APPENDIX
A.1 DERIVATION OF A cos.
The A cos in section 4.3 is calculated as below: (1) A cos cos(S (1) MMa)) cos(S) : Mrr))
(F((t) + F(()) (Mrs Mexu)
(Fitt) + =((0)) Mrs
((t) -11Mcall
|
||
E(tt
Mry + Fo Meyu F(()) Mr;
+ =F()
Megu F((t).
ko Mr, F(()
Mry (7)
((())
||rr,||
F'())
((l) ku
Meey
Heya
(s()
Since FOO) are orthogonal to Mecu (there overlap between them), ko Mezu is equal to 0, and Pry is unit vector, thus Eq. can be rewrite as:
A cos cos(s( Hex) cos(s Mr,)
(t)
P Megu
(8)
12

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Published as a conference paper at ICLR 2025
Table 4: Performance comparison the PlantVillage dataset. "** indicates results that implemented using the official code.
Accuracy in cach session (%)
Method
PD +
2
3
4
Fi-CNN
99.89
82.33
36. 11
34.38
28.53
71,47
CEC*
99.53
92.46
90.13
88.35
85.01
14.52
FACT*
99.47
93.33
90.69
90.00
86.15
13.32
TEEN*
99.50
90.83
90.56
86.38
85.19
14.31
BiDist*
99.51
91.25
84.14
81.18
78.97
20.54
NC-FSCIL* *
99.48
96.05
93.59
92.88
89.13
10.35
OrCo*
99.58
84.25
75.24
64.77
60.82
38.76
PA (ours)
99.50
95.83
93.45
93.24
90.51
8.99
This is consistent with the result presented in Section 4.3.
A.2 DERIVATION OF EQ. IN THE PAPER.
The class separability section 4.4 written as:
Me (Bes + 5)113
CS
Σ Em (1 cos(fmi Mc. )) + E 1 (1 cos Mej He, + Pe. + 82 + 2 6 Prc. 2 pc. (Pe, + 8) Ex= (1 cf (c.)) )) + N (1 cos(fu; (efB()ee))) Mey))
Since 8 is orthogonal to Mc, and Pag and peg unit vector, Eq. 10 is rewritten as:
1+ 22 + 0² 2 Me: Pe;
CS = NN, m=1 (1 cos( fmi Hc.) + N - cos(fuiHee))
(10)
1 8 - Pc. (2 - flc.)
Σ (1 cos((mi: Fm Mc,)) )) + Ex' (1 cos((oijee)))
which aligns with Eq. in the paper.
A.3 PERFORMANCE ON THE PLANTVILLAGE DATASET
We present the detailed classification accuracy the PlantVillage dataset in Table 4 The results show that our method significantly outperforms other methods the final session and achieves the smallest performance drop.
A.4 PERFORMANCE ON THE TREE-OP OREEE-F-LAAE LIFE DATASET
The detailed results on the Tree-of-Liffe dataset are shown in Table 5 Our proposed PA method out- performs most methods while delivering comparable performance (slightly lower) than NC-FSCIL.. Unlike NC-FSCIL, however, method does not require prior knowledge of the total number of unknown classes, making it more scalable and practical.
B DATASETS STATISTICS
CUB200. The CUB-200-2011 dataset contains 11,78 images of birds, categorized into 200 fine- grained subclasses (species) and 37 broader classes (families). PlantVillage. The VillaVillage dataset contains 54,306 images plant leaves, categorized into 38 fine-grained subclasses (species) and 14 broader classes (families). The images include healthy and diseased leaves, making it widely used benchmark for plant disease classification and recognition tasks. Tree-of-Life. We randomly sampled 13,487 images from 100 species in the Tree-of-LLif dataset, spanning 42 families.
13

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Table 5: Performance comparison the Tree-of-Liife dataset. "** indicates results that implemented using the official code.
Accuracy in each session (%)
Method
PD +
0
2
3
5
6
Ft-CNN
49.98
41.25
36.53
34.42
31.64
31.20
29.15
28.32
27.91
22.07
CEC
49.68
43.35
39.02
37.71
35.56
34.87
32.41
32.21
29.33
20.35
FACT*
48.33
45.25
39.25
39.00
35.20
35.33
32.17
32.43
29.71
18.62
TEEN#
49.33
43.00
38.25
37.60
34.40
33.83
32.00
33.00
30.57
18.76
BiDist
49.67
45.43
41.25
38.44
35.80
33.45
33.33
32.15
30.86
18.81
NC-FSCII.*
49.89
46.52
42.08
41.23
37.56
36.49
34.45
34.21
32.42
17.47
OrCo*
49.33
45.14
39.25
35.56
33.20
30.91
30.83
27.54
25.86
23.47
PA (ours)
49.67
46.75
41.75
41.00
37.68
36.67
34.33
34.57
32.14
17.53
This dataset exhibits high diversity, making it a challenging benchmark for the few-shot incremental learning task.
14


Reviews:
[{"review_id": "UuBtgaEvS1", "reviewer": "Reviewer_qswS", "summary": "This paper studies \"fine-grained\" class-incremental learning in a few-shot setting. The authors argue that under such a setting, the model would easily suffer overfitting (to new classes) or forgetting (of old classes). They thus propose to leverage hierarchical-class information (e.g., family-species) to encourage knowledge sharing across species of the same family while learning discriminative information. The paper proposes two new learning strategies for this purpose: RPL and RPM. In three small-scale experiments, the proposed approach demonstrated improved performance.", "questions": "Q1. What is the detail of the Feature decomposition strategy (Line 400)? CAM and Grad-CAM are methods to visualize a classifier. How did the authors use their saliency responses to get accuracy in Table 2?\n\nQ2. BioCLIP is a foundation model pre-trained on over 400K species. Can the authors provide more details about the argument that \"it struggles with continual learning and tend to misclassify closely related species?\" (Line 126)", "limitations": "", "rating": 3, "confidence": 4, "soundness": 1, "presentation": 1, "contribution": 2, "strengths": "S1. The paper focuses on an interesting and challenging problem. \n\nS2. The paper points out several insights that future research on fine-grained incremental learning could leverage.", "weaknesses": "W1. It is unclear or not intuitive why when new classes and old classes are similar (so have shearable information), incremental learning will lead to forgetting. \n\nW2. The technical/implementation details are not clear and not optimally designed. First, ResNet18 seems to be a too-weak backbone, especially for fine-grained problems. The authors may consider transformers (DINO, DINO-v2, BioCLIP visual encoder) or ResNet50 at least. Second, there is no information about whether the backbone is pre-trained, and if so, on what dataset. Third, it seems the feature backbone is only updated during the base training stage (Eq 1). If so, suppose the linear classifiers of the base (old) classes are frozen during continual learning, I see no reason why the old classes will be forgotten. Fourth, do the authors impose an orthogonal constraint in Line 270?\n\nW3. My major concern is the approach itself. The design seems to be quite ad hoc without justifications. First, I'm not sure why computing the residual between the linear classifiers (or prototypes) and feature vectors makes sense. Please note that the prototypes can simply be the feature vectors times a scalar. Why does the residual contain information on \"the secondary discriminative features likely representing traits shared across the family?\" Second, why does adding the residual to feature vectors of the new species make sense? What do the new feature vectors encode? Third, the meanings of the decompositions and terms introduced in Lines 270 - 290 are not clear or justified. Finally, if the residual is added to features of the new species during training, how about the inference stage?\n\nW4. The experiment details are missing; the experimental design can be improved. For example, no dataset statistics are provided. No information about how the authors obtained the family information. The PlantVillage dataset seems to be too small; the authors could consider using families in iNaturalist. No information about how the Tree of Life Dataset (with ~400K species) is subsampled. How do the authors ensure that all the families are seen during the base training time? Finally, experiments on no more than 200 species are a bit too small. \n\n=== Minor ===\n\nW5. There are many missing references, for example, no references to the datasets in the introduction; no reference to \"Inspired by human’s reference-based learning mechanism.\"\n\nW6. The related work seems to miss one topic, generalized few-shot learning, such as \"Generalized zero-and few-shot learning via aligned variational autoencoders, CVPR 2019.\" Will this line of work resolve the problem in Line 138?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "h23erxouEU", "reviewer": "Reviewer_P2DQ", "summary": "The work observes that a limitation of incremental learning is confusion with closely related species (like species from same family) and therefore makes use of family label of each species to overcome that. The method learns one family level prototypes representing shared traits, and species level prototypes representing species specific traits. In the initial learning stage prototypes are learned through a method introduced called Residual Prototype Learning and during incremental learning stages a method called Residual Prototype Mixing is introduced.", "questions": "1.\tLine 274, how the assumption that prototype do not contain background information is made? Is it not possible that both family level and species level prototypes may contain background information.\n2.\tLine 275 and 276, how can it be assumed that the species level prototypes (mu_ck) contain both family-shared and species-unique information, while in the RPL (line 231 and 232) it is assumed they capture the most species unique traits.\n3.\tIn section 4.3, it is not clear from Equation 3 how the delta of cosine similarity is optimized as mentioned in line 289. Is it happening implicitly when we optimize as in Equation 3.\n4.\tIn section 5.3 Feature Distribution subsection, does “without PA” mean the baseline model from Table 1, if so, please mention it explicitly\n5.\tThroughout the main paper, I’m assuming the cosine similarity between three vectors as in Equation 2 and 3 means the summation (or average?) of cosine similarity of the feature vector with respect to the two prototypes, but it would be better to explicitly mention it.\n6.\tIn table 3, PA’s performance is bolded. If the best performing method at each session can be bolded instead that would improve the readability of the table, making it easy to understand which method is doing better as I can see in Table 3, PA is not doing the best in all sessions (can also consider highlighting first, second and third best differently)", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1.\tExtensive comparison with multiple relevant baselines for CUB200.\n2.\tFamily information of any species is easy to obtain, so using that to improve incremental learning makes sense.", "weaknesses": "1.\tBasis for some assumptions made are not clearly explained. Please refer to the questions.\n2.\tNot every baseline is considered for all three datasets. No particular reason is provided for skipping several baselines. Particularly I believe NC-FSCIL is a necessary baseline since its performance is pretty close to PA without having to use family label. If the skipped methods are not suitable for Tree-Of-Life and Plant-Village, please explain.\n3.\tNo discussion of a possible limitation: In RPM are we assuming no new species is introduced whose family is not seen during the initial learning phase. If so, that is possible limitation of the work and it has to be discussed.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "kWl5yptJNc", "reviewer": "Reviewer_KRan", "summary": "The authors present a novel approach to handle the few-shot class-incremental learning problem, specifically in species classification. The authors take advantage of the hierarchical taxonomic relationship between species and use their family label to transfer knowledge from existing species and new species. Their approach, named Prototype Antithesis (PA), employs Residual Prototype Learning (RPL) to learn unique prototypes per species and novel residual prototypes to represent shared traits between species in a family. To further improve the model’s discriminating ability, they utilize Residual Prototype Mixing to generate synthetic samples for data augmented training. They show SOTA performance on three biological datasets: CUB200, PlantVillage, and Tree-of-Life.", "questions": "- Could the author help clarify my confusion about the Feature decomposition strategy in section 5.2\n- See weaknesses.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "- They show the generalization of their ability by evaluating on three biological image datasets.\n- The authors present novel residual prototype learning and residual prototype mixing training methods that improve class separating and alleviate catastrophic forgetting.\n- They present a theoretical analysis of their method, focusing on class-separation.\n- An ablation study shows the quantitative effectiveness of their residual prototype learning and mixing methods.\n- Their method shows stronger performance than others in the later stages of the incremental learning.", "weaknesses": "- The feature decomposition strategy section is convoluted, so Table 2 doesn’t make sense to me. I understand that there are high response and secondary response regions being extracted, but I’m confused how they are used to obtain the values in Table 2.\n- You have all of your model’s accuracies bolded in Table 3, but at several columns, your model is not the best (columns 0, 1, 2, and the PD). Please correct.\n- The method is only evaluated on 100 classes in the tree-of-life dataset. This should be run multiple times to ensure the robustness across different sections of the dataset given its size.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "thJR605OYB", "reviewer": "Reviewer_Zao3", "summary": "The article proposes a Prototype Antithesis (PA) method, which first learns species-unique and family-shared semantics of original classes. For new classes, it promotes learning by mixing family-shared features while preserving the model's ability to discriminate original classes. Experimental results validate the effectiveness of this method.", "questions": "The technical details are unclear; please see the weaknesses section.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper is well-organized.\n2. The literature review is detailed and comprehensive.\n3. The suite of experiments is admittedly comprehensive.", "weaknesses": "1. There are three variable in cosin similarity calculation, please explain how the cosine similarity is specifically calculated in Eq 2 for  $\\hat{Y}_{i}^r=cos(R_i, \\theta_c, \\theta_r)$ and $\\hat{Y}_i^0 = cos(F_i^0; \\theta_c, \\theta_r)$.\n2. How is the family-level label defined? Is this label provided by the dataset?\n3. What is the number of features for $\\theta_r$, and does this number change during training?\n4. Why does mixing the residual prototypes of old species with new species features encourage the model to capture the unique characteristics of the new species?\n5. Does the method require integrating residual features during the inference phase?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)