SAGE: Salient Factor Discovery and Generation with Visual Foundation Representations

Shuang Liang; Lejun Liao; Shiyuan Zhang; Max C. Zhang; Xiaolong Luo; Han Wang; Stefano Anzellotti; Yuan Yuan

cs.CVSep 30, 2026

SAGE: Salient Factor Discovery and Generation with Visual Foundation Representations

Authors: Shuang Liang, Lejun Liao, Shiyuan Zhang, Max C. Zhang, Xiaolong Luo, Han Wang, Stefano Anzellotti, Yuan Yuan

Organizations: HKU · Boston College · University of Virginia · Harvard University

Abstract

Given a target dataset, such as faces with eyeglasses, and a background dataset, such as faces without, contrastive analysis separates \textit{salient} factors specific to the target from \textit{common} content shared by both. We aim for salient representations that capture target-specific detail in each image, such as the shape, color, and position of the glasses, so that they reveal subtypes without subtype labels and guide the generation of new examples of a discovered subtype, even one with no name or text description. We introduce SAGE, which learns both factors directly in the high-dimensional spatial latent of a frozen representation autoencoder and conditions a diffusion transformer on the learned salient representation of a reference image. On Digits-ImageNet and FFHQ eyeglasses, SAGE combines high-fidelity \textit{reconstruction} (rFID below $2$ ) with unsupervised \textit{subtype discovery}, recovering the digits better than baselines (probe accuracy $0.950$ vs.\ at most $0.281$ ) and revealing eyewear types, finer sunglasses styles, and mislabeled images; salient-conditioned \textit{generation} raises Digits-ImageNet subtype accuracy over the unfactorized latent ( $90.5\%$ vs.\ $27.7\%$ ) and diversity on both datasets. On retinal OCT, SAGE's salient space separates three diseases using only normal/disease labels.

Figures & tables

Figure 1: Overview of SAGE. Given background (BG) and target (TG) datasets, SAGE uses only dataset labels to decompose frozen self-supervised visual representations into common and salient factors, capturing shared content and target-specific variation, respectively. The decomposition supports high-fidelity reconstruction, subtype discovery in the salient space, and salient-conditioned generation that preserves target attributes while varying common content.

Figure 2: Stage 1 of SAGE for salient factor discovery. Trainable common and salient encoders split latents of the frozen RAEv2 encoder (DINOv3); the frozen RAEv2 decoder maps factors back to images. Top: reconstruction ( $\mathcal{L}_{\mathrm{rec}}$ ) and swap adversarial training ( $\mathcal{L}^{G}_{\mathrm{swap}}$ ). Bottom: swapped images are re-encoded for $\mathcal{L}_{\mathrm{cyc}}$ and $\mathcal{L}_{\mathrm{cNCE}}$ , while $\mathcal{L}_{\mathrm{lsc}}$ and the sparsity priors act directly on the factors. Snowflakes and flames mark frozen and trainable modules.

Figure 3: Stage 2 of SAGE for salient-conditioned generation. The frozen RAEv2 and salient encoders extract a reference salient representation, max-pooled to $c_{s}$ . An RAEv2-initialized diffusion transformer is trained with conditional flow matching in this latent. Top right: generated samples; each row keeps the eyewear type or digit of the reference at left while faces and scenes vary.

	Digits-ImageNet							FFHQ
	Reconstruction				Salient representation			Reconstruction				Salient representation
Method	rFID ↓	PSNR ↑	SSIM ↑	LPIPS ↓	BG/TG Sil. ↑	LP Acc. ↑	ARI/NMI ↑	rFID ↓	PSNR ↑	SSIM ↑	LPIPS ↓	BG/TG Sil. ↑	LP Acc. ↑	ARI/NMI ↑
cVAE ( Abid and Zou, 2019 )	151.0	18.33	0.478	0.673	0.006	0.135	0.001/0.003	188.2	18.42	0.567	0.590	0.027	0.898	0.001/0.001
SepVAE ( Louiset et al., 2023 )	162.5	17.50	0.461	0.696	0.006	0.145	0.009/0.019	197.4	17.67	0.552	0.598	0.206	0.897	-0.001/0.000
Double-InfoGAN ( Carton et al., 2024 )	171.4	15.97	0.375	0.668	0.013	0.281	0.001/0.003	122.5	16.73	0.424	0.458	0.050	0.964	0.298/0.299
SepCLR ( Louiset et al., 2024 )	N/A	N/A	N/A	N/A	0.546	0.180	0.000/0.001	N/A	N/A	N/A	N/A	0.752	0.970	0.822/0.696

Table 1: Benchmark comparison on Digits-ImageNet and FFHQ. rFID: reconstruction Fréchet Inception Distance; N/A: no decoder; Double-InfoGAN uses its native

128\times 128

resolution; all other methods use

256\times 256

. BG/TG Sil.: silhouette score of background versus target salient representations (Appendix Table 5 ). LP Acc.: linear-probe subtype accuracy, five-fold on Digits-ImageNet and balanced on FFHQ (Appendix B.4 ). ARI/NMI:

k

-means on the

\ell_{2}

-normalized, unprojected salient representations, with

k

set to the subtype count (Appendix B.5 ). FFHQ labels are audit-corrected.

Figure 4: Reconstruction, common-only, and salient-only decoding. Columns show the input, the full reconstruction $D(z_{c},z_{s})$ , common-only decoding $D(z_{c},\mathbf{0})$ , and salient-only decoding $D(\mathbf{0},z_{s})$ on Digits-ImageNet (left) and FFHQ (right); rows compare cVAE, SepVAE, Double-InfoGAN, and SAGE. A clean salient factor renders only the digit or eyeglasses.

Figure 5: Subtype discovery in the salient space. Top: t-SNE of SAGE’s salient representations separates background and target images (a, d) and organizes digit and eyewear subtypes (b, e), whereas raw DINOv3 features mix them (c, f). Bottom: examples from the $k$ -means clusters marked in (b, e) show coherent digit and eyewear groups (g, h); FFHQ cluster C contains images mislabeled as wearing glasses, and red points are audited no-glasses images.

Figure 7: Salient interpolation. For two held-out target images $x^{A}$ and $x^{B}$ , salient-only rows decode $D(z_{c}^{A},z_{s}^{\alpha})$ with $z_{s}^{\alpha}=(1-\alpha)z_{s}^{A}+\alpha z_{s}^{B}$ ; full-latent rows interpolate both factors.

Figure 8: OCT disease subtypes and salient edits. Left: salient t-SNE of disease scans, colored by disease subtype labels unused in training. BG $\to$ TG adds a disease scan’s salient factor to a normal scan; TG $\to$ BG removes a disease scan’s salient factor.

Dataset	Conditioning	Uncond. gFID ↓	Cond. gFID ↓	Subtype Acc. ↑	Vendi ↑	Cos-to-ref ↓
Digits-ImageNet	Real images	–	–	99.3	15.79	–
	Raw DINOv3 GMP	8.86	2.40	27.70	2.65	0.794
	SAGE Salient GMP	4.76	4.09	90.48	12.50	0.116
FFHQ	Real images	–	–	99.1	10.09	–
	Raw DINOv3 GMP	16.07	11.17	98.5	2.32	0.779
	SAGE Salient GMP	15.80	16.03	96.6	6.96	0.470

Table 2: Salient-conditioned generation. Raw DINOv3 GMP conditions on the unfactorized latent. Subtype Acc.: share of samples assigned the reference subtype by a DINOv3-L classifier trained on real targets. Vendi (diversity) and Cos-to-ref (reference similarity) use 16 samples per reference and DINOv2-L features. Real images : classifier accuracy and within-subtype Vendi (Appendix D.2 ).

	Full Reconstruction				Common-only		Salient Linear Probe		Clustering
Configuration	rFID ↓	PSNR ↑	SSIM ↑	LPIPS ↓	rFID ↓	SSIM ↑	Digit ↑	ImageNet ↓	ARI/NMI ↑
SAGE (all objectives)	1.78	20.56	0.571	0.216	1.89	0.569	0.950	0.045	0.337/0.472
w/o swap adversarial ( $\mathcal{L}^{G}_{\mathrm{swap}}$ )	1.01	20.67	0.566	0.210	4.88	0.544	0.123	0.169	0.000/0.001
w/o target sparsity ( $\mathcal{L}_{\mathrm{TG\text{-}sp}}$ )	1.15	21.06	0.598	0.193	1.36	0.585	0.923	0.302	0.274/0.409
w/o Cycle NCE ( $\mathcal{L}_{\mathrm{cNCE}}$ )	1.29	20.78	0.576	0.205	1.58	0.573	0.950	0.100	0.348/0.479
w/o image-space cycle consistency ( $\mathcal{L}_{\mathrm{cyc}}$ )	2.84	19.88	0.562	0.236	3.46	0.559	0.951	0.086	0.360/0.496

Table 3: Loss ablations on Digits-ImageNet. Common-only: rFID and SSIM of

D(z_{c},\mathbf{0})

for target images against their digit-free ImageNet backgrounds. Salient probes measure digit identity (target content, higher is better) and ImageNet category (shared content, lower is better). ARI/NMI are computed as in Table 1 ; their standard deviations over three

k

-means seeds are at most

0.0007

Appendix figures & tables16 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 9: Additional SAGE factor-decoding examples. Randomly selected images. For Digits-ImageNet (a–d) and FFHQ (e–h), complete decoding reconstructs the target image, common-only decoding removes the digit or eyeglasses while preserving the scene or identity, and salient-only decoding retains the digit’s shape and location or the specific eyewear style with little common content.

		Digits-ImageNet ( $k=10$ , $n=24{,}739$ )			FFHQ ( $k=2$ , $n=3{,}398$ )			FFHQ ( $k=3$ , $n=3{,}437$ )
Method	Dim.	PCA-2D	PCA-50	Raw	PCA-2D	PCA-50	Raw	PCA-2D	PCA-50	Raw
ARI ↑
cVAE	64	0.000	0.001	0.001	0.001	0.000	0.001	0.036	0.017	0.016
SepVAE	64	0.006	0.009	0.009	-0.001	-0.001	-0.001	0.012	0.019	0.017
Double-InfoGAN	64	0.005	0.001	0.001	0.324	0.292	0.298	0.247	0.303	0.303
SepCLR	32	0.000	0.000	0.000	0.808	0.822	0.822	0.441	0.455	0.455

Appendix

Table 4: Clustering readout ablation on

\ell_{2}

-normalized salient representations (ARI/NMI). All vectors are normalized before optional projection. Raw denotes no dimensionality reduction; PCA-50 retains

\min(50,\mathrm{Dim.})

components. Scores are means over

k

-means seeds

\{0,1,2\}

. FFHQ uses manually verified annotations, with 39 no-glasses images excluded for

k=2

and included for

k=3

Figure 10: t-SNE visualizations of salient representations across contrastive analysis methods. Columns show cVAE, SepVAE, Double-InfoGAN, SepCLR, DINOv3 + SepCLR, and SAGE. Rows show Digits-ImageNet background-versus-target and digit-colored target representations, then their FFHQ counterparts with audited eyewear labels. Blue/orange denote background/target; green/purple/red denote reading glasses/sunglasses/audited no-glasses images.

Method	Digits t-SNE	Digits original	FFHQ t-SNE	FFHQ original
cVAE	0.022	0.006	0.004	0.027
SepVAE	0.003	0.006	0.394	0.206
Double-InfoGAN	0.248	0.013	0.188	0.050
SepCLR	0.381	0.546	0.449	0.752
DINOv3 + SepCLR	0.365	0.642	0.387	0.551
SAGE	0.417	0.820	0.462	0.873

Appendix

Table 5: Silhouette scores for BG-vs.-TG salient separation in t-SNE and original spaces. Scores treat background (BG) and target (TG) salient representations as the two groups. The silhouette coefficient ( Rousseeuw, 1987 ) measures within-group cohesion relative to separation; higher is better.

Figure 11: Fine-grained sunglasses structure in SAGE’s salient representation. Left: FFHQ target embeddings colored by audited eyewear labels, with C1–C3 marking subgroups within sunglasses. Right: example images from each subgroup: Wayfarer-like (C1, $n=168$ ), mixed styles (C2, $n=390$ ), and Aviator-like (C3, $n=175$ ). Group names describe the displayed examples; no style labels are used in training.

Figure 12: Azure eyewear annotation errors identified by manual inspection of FFHQ. Left: t-SNE of SAGE’s target salient representations, colored by manually verified labels; outlined points mark the 125 Azure label disagreements. Right: all 125 corrected images, grouped by verified label: sunglasses (65), no glasses (39), and reading glasses (21). Tile titles give the original Azure category and image ID.

Figure 13: Remaining SAGE errors among manually verified no-glasses FFHQ images. (a) Target salient-space t-SNE, colored by audited labels: reading glasses (green, 2,636), sunglasses (purple, 762), and no glasses (red, 39). Black-outlined red points identify five no-glasses cases associated with glasses groups. (b) Each numbered row shows one query and its six nearest neighbors; border colors indicate human labels and values report cosine similarity. Arrows mark the group each query is associated with. (c) The other 34 no-glasses images form a separate group.

Figure 14: Additional salient swapping and erasure examples. Left (BG $\rightarrow$ TG): $D(z_{c}^{b},z_{s}^{t})$ combines a background image’s common factor with a target image’s salient factor. Right (TG $\rightarrow$ BG): $D(z_{c}^{t},\mathbf{0})$ decodes the target common factor alone. Rows show Digits-ImageNet and FFHQ examples; red borders mark decoded outputs.

Method	rFID ↓	PSNR ↑	SSIM ↑	LPIPS ↓	LP Acc. ↑	ARI/NMI ↑
SAGE (Ours)	27.21	24.55	0.448	0.273	0.960	0.415/0.415

Appendix

Table 6: Stage-1 metrics on OCT-Kermany. Evaluation uses the official test set ( Kermany et al., 2018 ) , with 250 normal images and 250 images from each of CNV, DME, and DRUSEN, from patients not included in training or validation. LP Acc. reports mean stratified five-fold linear-probe accuracy on the 750 target images; ARI/NMI use

k

-means directly on SAGE’s

\ell_{2}

-normalized, 1,024-dimensional salient representations obtained by global max pooling (GMP), without dimensionality reduction. With only 1,000 evaluation images, rFID is sensitive to finite-sample bias and is not directly comparable to scores computed on larger evaluation sets.

Figure 15: Generation conditioned on a reference image. For FFHQ (left) and Digits-ImageNet (right), each column shows a reference (top) and one sample conditioned on Raw DINOv3 GMP (middle) or on SAGE’s salient representation (bottom).

	Full reconstruction				Common-only		Digit LP		ImageNet LP		Clustering
Configuration	rFID ↓	PSNR ↑	SSIM ↑	LPIPS ↓	rFID ↓	SSIM ↑	$z_{s}$ ↑	$z_{c}$ ↓	$z_{c}$ ↑	$z_{s}$ ↓	ARI/NMI ↑
Raw $z$	0.39	22.18	0.613	0.157	N/A	N/A	0.328	N/A	0.765	N/A	0.000/0.001
SAGE (all)	1.78	20.56	0.571	0.216	1.89	0.569	0.950	0.336	0.765	0.045	0.337/0.472
w/o $\mathcal{L}^{G}_{\mathrm{swap}}$	1.01	20.67	0.566	0.210	4.88	0.544	0.123	0.356	0.763	0.169	0.000/0.001
w/o $\mathcal{L}_{\mathrm{TG\text{-}sp}}$	1.15	21.06	0.598	0.193	1.36	0.585	0.923	0.656	0.758	0.302	0.274/0.409
w/o $\mathcal{L}_{\mathrm{cNCE}}$	1.29	20.78	0.576	0.205	1.58	0.573	0.950	0.335	0.767	0.100	0.348/0.479

Appendix

Table 7: Full loss ablation of SAGE on Digits-ImageNet. ARI/NMI use Euclidean

k

-means on

\ell_{2}

-normalized original salient representations, without dimensionality reduction (Appendix B.5 ). ARI/NMI are means over

k

-means seeds

\{0,1,2\}

; standard deviations are omitted because they are at most

0.0007

. Digit/ImageNet LP: linear-probe accuracy for digit or ImageNet labels from the indicated representation; Raw

z

: the unfactorized latent, whose probes use

z

itself. Objectives are named as in Table 3 .

Split	NORMAL	CNV	DME	DRUSEN	Total	Patients
Train	46,026	33,485	10,213	7,754	97,478	4,772
Val.	5,114	3,720	1,135	862	10,831	3,329
Test	250	250	250	250	1,000	635

Appendix

Table 8: OCT-Kermany split statistics after preprocessing. CNV, DME, and DRUSEN are the target diseases; normal scans are background.

Component	DINOv3 + SepCLR	SAGE
Frozen visual backbone	DINOv3-L (MLS)	DINOv3-L (MLS)
Latent normalization	RAEv2	RAEv2
Common/salient Transformers	Shared architecture	Shared architecture
Evaluation pooling	GAP	GMP
Evaluation representation	32 dimensions	1,024 dimensions
Training objectives	SepCLR objectives	SAGE objectives

Appendix

Table 9: Components of DINOv3 + SepCLR and SAGE.

Figure 16: Common and salient encoder architecture. Input and output convolutions use kernel size 3. The Transformer block applies layer normalization, self-attention, and a feed-forward network (FFN), with residual connections around the attention and FFN sublayers. The two encoders produce the common and salient representations, respectively. In the salient encoder, both the weights and biases of Conv_out are initialized to zero.

Figure 17: Shared multi-expert discriminator architecture. A frozen DINOv1 ViT-S/8 backbone provides features from layers 2, 5, 8, 11, and the final layer to separate background (BG) and target (TG) experts. Each expert contains trainable heads for the corresponding feature levels, enabling group-specific real/fake discrimination. Snowflake and flame symbols indicate frozen and trainable components, respectively.

Figure 18: Cycle NCE pair construction. “P” denotes corresponding same-factor positive pairs, “N” denotes cross-factor negative pairs, and blank cells denote excluded same-factor off-diagonal pairs.

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