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TRIPROBE: Probing Task Separability Beyond Classification for XAI
Amirhossein Sadough, Freek Hens, Aleksa Bokšan, Mohammad Mahdi Dehshibi, Mahyar Shahsavari
TL;DR
Downstream accuracy offers little insight into where task-separability bottlenecks arise. TriProbe decomposes multi-task problems into binary subtasks and probes raw inputs, learned representations, and final outputs; on Roshambo sEMG, it identifies pairwise and stage-specific breakdowns that can inform data collection, validation, and architecture design.
Problem
Downstream-accuracy-focused evaluation provides little insight into where task-separability bottlenecks emerge, complicating data collection and model design.
Source: §I · p. 1Method
TriProbe decomposes multi-task problems into binary subtasks and traces separability across input, learned-representation, and final-output stages using a common metric.
Source: §I · p. 1, A. Foundational Probe · p. 2Results
On Roshambo sEMG, P–S is the hardest pair, while R–S shows good latent separation but degrades at the Final probe.
Source: B. Results · pp. 3–4Takeaways & Limitations
Stage-wise diagnosis can support early data-quality assessment, validation of experimental findings, and architecture refinement through interpretable separability analysis.
Source: §IV · p. 5, §V · p. 5
Abstract
from arXiv · showhide
Modern evaluation of learning pipelines often reduces to downstream accuracy, leaving open the question of why tasks succeed or fail. TriProbe addresses this gap with a multi-level probing framework for explainable diagnosis of task separability. Rather than treating models as black boxes, TriProbe traces how separability evolves across inputs, learned features, and final classifiers. It decomposes multi-task problems into binary subtasks and applies three complementary probes: a Foundational Probe on input spaces, a Latent Probe on feature representations, and a Final Probe on classifier outputs. Using Maximum Fisher's Discriminant Ratio as a principled separability metric, TriProbe identifies bottlenecks and affected task pairs. Experiments on the Roshambo sEMG benchmark show how TriProbe reveals hidden breakdowns, guiding data collection, validation, and architecture design.
I. INTRODUCTION
Existing evaluation often centers on downstream accuracy, offering little insight into where task separability bottlenecks emerge. TriProbe traces separability across learning stages to diagnose difficult task pairs and inform data collection and model design.
I. INTRODUCTION
- Downstream-accuracy-focused evaluation provides little insight into where separability bottlenecks emerge, complicating data collection and model design.Source: §I · p. 1
- TriProbe traces how separability can improve or deteriorate across successive learning stages and identifies the task pairs most affected.Source: §I · p. 1
- The framework decomposes multi-task classification into binary subtasks and probes input and handcrafted-feature spaces, learned latent representations, and network output space.Source: §I · p. 1
- On sEMG gesture recognition, TriProbe uncovers bottlenecks consistent with downstream performance and prior studies while showing how separability evolves across stages.Source: §I · p. 1
II. PROPOSED MULTI-LEVEL PROBING
TriProbe combines binary task decomposition with three probes to diagnose whether separability limitations arise in data, representations, or final stages.
II. PROPOSED MULTI-LEVEL PROBING
- TriProbe converts a multi-task problem into binary subtasks to assess the intrinsic difficulty of each class pair without indirect cues from unrelated classes.Source: §II · p. 2
- The Foundational Probe assesses raw data and handcrafted features, the Latent Probe evaluates feature-extractor representations, and the Final Probe analyzes final-level separability.Source: §II · p. 2
- Together, the probes diagnose whether limitations arise from data, representation, or final stages of the pipeline.Source: §II · p. 2
A. Foundational Probe
The Foundational Probe compares raw inputs and handcrafted features using Maximum Fisher’s Discriminant Ratio to assess class separability.
A. Foundational Probe
- Raw multi-channel samples and engineered feature vectors form the two input spaces assessed by the Foundational Probe.Source: A. Foundational Probe · p. 2
- Maximum Fisher’s Discriminant Ratio (F1) identifies the most discriminative feature dimension for separating a target class from a reference.Source: A. Foundational Probe · p. 2
- The overall score takes the maximum feature-level Fisher score; higher values indicate stronger separability, while low values suggest intrinsic overlap.Source: A. Foundational Probe · p. 2
B. Latent Probe
The Latent Probe measures pairwise separability in learned representations using class-specific autoencoder encodings.
B. Latent Probe
- The Latent Probe evaluates task separability in learned representations, and its feature extractor can use contemporary architectures.Source: B. Latent Probe · p. 2
- This work uses an autoencoder encoder after satisfactory reconstruction, discarding the decoder to avoid bias from downstream task objectives such as classification.Source: B. Latent Probe · p. 2
- A dedicated autoencoder is trained for each class to prevent reconstruction from exploiting cues from unrelated classes and preserve class-specific representation quality.Source: B. Latent Probe · p. 2
- The resulting class-specific latent feature matrices are compared pairwise to measure separability in the latent space.Source: B. Latent Probe · p. 2
C. Final Probe
The Final Probe evaluates separability at the penultimate layer, isolating the body network’s representations before the final decision. TriProbe interprets probe differences to locate pipeline bottlenecks.
C. Final Probe
- The Final Probe captures penultimate-layer vectors to assess how effectively the body network prepares representations for the final decision.A generic MLP body is placed after the autoencoder latent representation; the probe sits at the interface with the final layer.Source: C. Final Probe · p. 2
- Comparing probe stages distinguishes body-level representation limits from intrinsic data limitations or feature-extraction bottlenecks.The Final Probe evaluates the body network, while the Foundational and Latent Probes assess earlier stages.Source: C. Final Probe · p. 3
D. Interpretation
- Low Foundational scores indicate intrinsic data complexity, while strong latent separability followed by weak final separability points to an intervening architectural issue.Stage-wise comparisons can guide data-quality assessment, architectural refinement, and interpretation of results.Source: D. Interpretation · p. 3
A. Setup
The Roshambo setup evaluates three gesture pairs using raw and handcrafted inputs, class-specific autoencoder representations, and pairwise final probes.
A. Setup
- The Roshambo benchmark includes 450 trials from ten participants, recorded with eight sEMG channels at 200 Hz.After discarding the first and last 600 ms, 400-sample windows with 50% overlap yielded 296 samples per class.Source: A. Setup · p. 3
- The Foundational Probe maps each 8×400 sample to 72 handcrafted features spanning temporal, spectral, and complexity characteristics.Features include entropy, zero crossings, RMS, median frequency, wavelet energy, and fractal dimension.Source: A. Setup · p. 3
- Three class-specific autoencoders produce 392-dimensional latent representations for pairwise separability analysis.The encoders are trained on all available samples for 300 epochs using Huber loss and Adam; the passage specifies the architecture by reference to Table I.Source: A. Setup · p. 3
- For each gesture pair, a frozen autoencoder feeds an MLP, with only the MLP trained and the probe placed between its body and final layer.This design aims to keep the AE representation fixed while assessing limitations in body representations or the final decision rule.Source: A. Setup · p. 3
B. Results
Across probes, Paper–Scissors is the hardest gesture pair, while other pair rankings vary slightly by representation. Stage comparisons also reveal pair-specific losses between latent and final probes.
F1 Validation
- F1 probe patterns broadly align with downstream performance, although raw data slightly favors R–P over R–S while handcrafted features reverse that ordering.Prior model variants likewise diverge slightly, with one favoring R–S and the other R–P.Source: B. Results · p. 3
- Foundational F1 can flag difficult subtasks early, including during data collection, and the authors present it as an effective separability probe.The authors argue that raw-level complexity should propagate through the pipeline rather than be contradicted downstream.Source: B. Results · p. 3
TriProbe Analysis
- Paper–Scissors is consistently the hardest pair, with substantial raw-data and handcrafted-feature overlap and the weakest overall separability.The result aligns across binary confusion matrices and prior multi-task evaluations.Source: B. Results · p. 3, Figure 3 · p. 4
- R–S separates well in the latent probe but degrades at the Final Probe, suggesting ineffective learning in the MLP body.The authors identify redesigning this stage as a possible way to mitigate the loss.Source: B. Results · p. 4
- P–S also degrades from Foundational to Latent probing, pointing to a need for stronger feature extraction.The stage-wise comparison tracks whether architectural changes improve or degrade performance relative to a baseline.Source: B. Results · p. 4
IV. DISCUSSION
TriProbe integrates established components to trace separability across stages and locate affected task pairs. The discussion frames it as a diagnostic aid for data collection and architecture design, while calling for broader evaluation.
IV. DISCUSSION
- Unlike approaches limited to one stage or individual predictions, TriProbe traces where separability is gained or lost across the pipeline.Its formulation is architecture-agnostic and task-independent, but validation so far is on an sEMG gesture-recognition dataset.Source: §IV · pp. 4–5
- TriProbe’s novelty is integrating established components into a unified, stage-consistent framework rather than proposing a new classifier or separability metric.It combines binary task decomposition with a common criterion to distinguish limitations arising from data, feature extraction, or the downstream classifier.Source: §IV · p. 4
- Raw and handcrafted-feature probes can support early data-quality assessment before costly large-scale collection or model training.If early-stage probes contradict downstream performance, the framework can flag potential overfitting, data leakage, or ineffective architectural choices.Source: §IV · p. 5
- The observed R–S loss from latent to final probing suggests the current MLP body may be ineffective, motivating alternative-architecture experiments.The authors call for evaluation across datasets and modalities to confirm generalizability and examine varying data complexities and architectures.Source: §IV · p. 5
V. CONCLUSION
TriProbe traces task separability across raw data, learned representations, and decision spaces to localize bottlenecks and identify challenging class pairs. Demonstrated on sEMG gesture recognition, it is designed to apply across machine-learning domains.
V. CONCLUSION
- TriProbe provides a unified framework for tracing task-separability difficulty across raw data, learned representations, and decision spaces.It offers stage-wise insights into how discriminative information evolves throughout the learning pipeline.Source: §V · p. 5
- Stage-wise separability analysis localizes bottlenecks and identifies challenging class pairs beyond downstream performance metrics alone.Source: §V · p. 5
- TriProbe serves as an early diagnostic for data quality and can validate experimental findings and guide architecture refinement.Source: §V · p. 5
- Although demonstrated on an sEMG gesture-recognition task, the framework is designed to be architecture-agnostic and applicable to other machine-learning domains.Source: §V · p. 5
- Future work will evaluate additional modalities and datasets, investigate alternative separability measures, and integrate TriProbe into adaptive learning pipelines.The stated long-term goal is a general-purpose, separability-driven methodology for interpretable and reliable AI.Source: §V · p. 5