| """Build the defence deck as .pptx from the same content as the Beamer source. |
| |
| Beamer is the primary deliverable; this exists so the deck can be handed to |
| someone who needs to edit it in PowerPoint. Numbers come from |
| slides/numbers.json (generated), figures from slides/figures/ (generated), so |
| the two decks cannot disagree with each other or with the thesis. |
| """ |
| from __future__ import annotations |
|
|
| import json |
| from pathlib import Path |
|
|
| from pptx import Presentation |
| from pptx.dml.color import RGBColor |
| from pptx.enum.text import PP_ALIGN |
| from pptx.util import Emu, Inches, Pt |
|
|
| ROOT = Path(__file__).resolve().parents[1] |
| SLIDES = ROOT / 'slides' |
| FIGS = SLIDES / 'figures' |
|
|
| INK = RGBColor(0x0B, 0x0B, 0x0B) |
| INK2 = RGBColor(0x52, 0x51, 0x4E) |
| MUTED = RGBColor(0x89, 0x87, 0x81) |
| MINORITY = RGBColor(0x2A, 0x78, 0xD6) |
| SURFACE = RGBColor(0xFC, 0xFC, 0xFB) |
|
|
| N = json.loads((SLIDES / 'numbers.json').read_text()) |
| W, H = Inches(13.333), Inches(7.5) |
|
|
|
|
| def background(slide): |
| fill = slide.background.fill |
| fill.solid() |
| fill.fore_color.rgb = SURFACE |
|
|
|
|
| def textbox(slide, left, top, width, height, text, size=18, color=INK, |
| bold=False, align=PP_ALIGN.LEFT, spacing=1.15): |
| box = slide.shapes.add_textbox(left, top, width, height) |
| frame = box.text_frame |
| frame.word_wrap = True |
| for i, line in enumerate(text.split('\n')): |
| para = frame.paragraphs[0] if i == 0 else frame.add_paragraph() |
| para.alignment = align |
| para.line_spacing = spacing |
| run = para.add_run() |
| run.text = line |
| run.font.size = Pt(size) |
| run.font.color.rgb = color |
| run.font.bold = bold |
| run.font.name = 'Calibri' |
| return box |
|
|
|
|
| def blank(prs): |
| slide = prs.slides.add_slide(prs.slide_layouts[6]) |
| background(slide) |
| return slide |
|
|
|
|
| def titled(prs, title): |
| slide = blank(prs) |
| textbox(slide, Inches(0.7), Inches(0.4), Inches(12.0), Inches(0.9), |
| title, size=30, color=INK, bold=True) |
| return slide |
|
|
|
|
| def picture(slide, name, top=Inches(1.45), height=Inches(4.6)): |
| path = FIGS / f'{name}.png' |
| pic = slide.shapes.add_picture(str(path), Inches(0), top, height=height) |
| pic.left = Emu(int((W - pic.width) / 2)) |
| return pic |
|
|
|
|
| def takeaway(slide, text, size=16): |
| textbox(slide, Inches(0.7), Inches(6.25), Inches(12.0), Inches(1.0), |
| text, size=size, color=INK2) |
|
|
|
|
| def bullets(slide, items, top=Inches(1.6), size=20, gap=0.78): |
| for i, item in enumerate(items): |
| textbox(slide, Inches(1.0), top + Inches(gap * i), Inches(11.4), |
| Inches(0.9), f'• {item}', size=size, color=INK) |
|
|
|
|
| def build(): |
| prs = Presentation() |
| prs.slide_width, prs.slide_height = W, H |
|
|
| s = blank(prs) |
| textbox(s, Inches(1.0), Inches(2.5), Inches(11.3), Inches(2.0), |
| 'Majority Accumulation and Minority False Matches\nin Locally Adapted Face Verification', |
| size=34, color=INK, bold=True, align=PP_ALIGN.CENTER) |
| textbox(s, Inches(1.0), Inches(4.6), Inches(11.3), Inches(0.6), |
| 'MSc thesis defence', size=18, color=MUTED, align=PP_ALIGN.CENTER) |
|
|
| s = titled(prs, 'Institutions adapt recognisers. They do not train them.') |
| bullets(s, ['Take a model pretrained on a very large public corpus.', |
| 'Fine-tune it on the identities you have already enrolled.', |
| 'Cheap, needs no new data, and reliably improves measured accuracy.']) |
| textbox(s, Inches(0.7), Inches(4.5), Inches(12.0), Inches(1.4), |
| f"But you do not choose who is in that set. In our corpus, " |
| f"{N['pctMale']}% of {N['Nidentities']} enrolled identities are male — " |
| f"not by decision, but because the cohort comes from engineering programmes.", |
| size=19, color=INK) |
| takeaway(s, 'The practitioner faces a fork that does not look like a fork: ' |
| 'use the data as it arrives, or curate it.') |
|
|
| s = titled(prs, 'The question') |
| textbox(s, Inches(1.0), Inches(2.1), Inches(11.3), Inches(1.4), |
| 'What does the natural choice cost,\nand who pays for it?', |
| size=32, color=MINORITY, bold=True, align=PP_ALIGN.CENTER) |
| bullets(s, ['RQ1 How does adaptation-set composition affect subgroup performance?', |
| 'RQ2 Do protocol choices change what we observe?', |
| 'RQ3 What in the representation explains it?'], top=Inches(4.3), size=18, gap=0.62) |
|
|
| s = titled(prs, 'Seven models. The metric everybody reports.') |
| picture(s, 's_hook_genuine') |
| takeaway(s, 'Flat. Overall accuracy spans 0.025 across every condition.') |
|
|
| s = titled(prs, 'The same seven models. The metric almost nobody reports.') |
| picture(s, 's_hook_impostor') |
| takeaway(s, f"The rate at which two different minority users are confused spans " |
| f"{N['FPRFFspan']}×, from {N['FPRFFbest']} to {N['FPRFFworst']}.") |
|
|
| s = titled(prs, 'Three things worth noticing') |
| bullets(s, ['The two impostor rates move in opposite directions. ' |
| 'A merely worse model would degrade both.', |
| 'The harm is entirely on the impostor side — minority users are not locked out, ' |
| 'they are let into each other’s accounts.', |
| f"The minority true-accept rate is highest in the worst condition ({N['TPRFworst']})."], |
| size=19, gap=1.05) |
| takeaway(s, 'An auditor checking whether minority users are wrongly rejected ' |
| 'would see nothing wrong at all.') |
|
|
| s = titled(prs, 'Why prior work cannot answer this') |
| bullets(s, ['The female share falls from 50% to 22%, and', |
| 'the female count falls from 87 to 39 — at the same time.']) |
| textbox(s, Inches(0.7), Inches(3.5), Inches(12.0), Inches(1.6), |
| 'Those imply opposite remedies: rebalance what you have, or go and collect more ' |
| 'minority data. Varying a ratio at fixed total size cannot separate them.', |
| size=19, color=INK) |
| takeaway(s, 'So we vary the two counts independently.') |
|
|
| s = titled(prs, 'The factorial design') |
| pic = s.shapes.add_picture(str(FIGS / 's_design.png'), Inches(0.6), Inches(1.5), height=Inches(4.9)) |
| textbox(s, Inches(7.6), Inches(1.8), Inches(5.2), Inches(4.0), |
| 'Four cells cross minority count with majority count.\n\n' |
| 'Across a row: adding majority identities.\n\n' |
| 'Down a column: adding minority identities.\n\n' |
| 'Fixed throughout: optimiser budget, identity splits, comparison manifests, ' |
| 'and the pretrained checkpoint.', size=17, color=INK) |
|
|
| s = titled(prs, 'Protocol discipline') |
| bullets(s, [f"{N['Nidentities']} audited identities ({N['Nmale']} male, {N['Nfemale']} female), " |
| f"{N['Nimages']} images.", |
| 'Four disjoint roles per fold: fit, select, operate (calibration only), test (reporting only).', |
| 'No calibration or test identity ever receives a gradient update.', |
| 'Thresholds fitted on operate, applied unchanged to test.'], size=18, gap=0.72) |
| takeaway(s, 'The central quantities are differences of a few thousandths. ' |
| 'Minor leakage would not support them.') |
|
|
| s = titled(prs, 'Both levers are real') |
| picture(s, 's_forest') |
| takeaway(s, f"Per identity added, the minority lever is {N['leverRatio']}× stronger " |
| f"than the majority one.") |
|
|
| s = titled(prs, 'And they interact') |
| s.shapes.add_picture(str(FIGS / 's_interaction.png'), Inches(0.6), Inches(1.5), height=Inches(4.9)) |
| textbox(s, Inches(7.6), Inches(2.0), Inches(5.2), Inches(3.6), |
| 'Majority accumulation is 2.2× more harmful when the minority is scarce.\n\n' |
| 'Minority collection is 2.4× more beneficial when the majority is plentiful.\n\n' |
| 'Each remedy is worth most exactly where the other is worth least.', |
| size=17, color=INK) |
|
|
| s = titled(prs, 'A result we had to withdraw') |
| picture(s, 's_seeds') |
| takeaway(s, f"At three seeds the accuracy gain read +0.0124 [+0.0051, +0.0187] — an interval " |
| f"excluding zero. At eight seeds it is {N['seedTPR']} (t = {N['seedTPRt']}). " |
| f"The minority harm survives at {N['seedFPR']} (t = {N['seedFPRt']}).", size=15) |
|
|
| s = titled(prs, 'What that changed') |
| textbox(s, Inches(0.7), Inches(1.7), Inches(12.0), Inches(1.6), |
| 'The earlier interval was honest but conditional: it described variation over ' |
| 'comparisons, not over retraining. Adding the omitted variance component decided ' |
| 'the contrast.', size=20, color=INK) |
| textbox(s, Inches(1.0), Inches(3.7), Inches(11.3), Inches(1.2), |
| 'There is no reliable accuracy to gain.\nOnly minority security to lose.', |
| size=28, color=MINORITY, bold=True, align=PP_ALIGN.CENTER) |
| takeaway(s, 'Skewed adaptation is also less predictable: 4.5× less stable in minority ' |
| 'false matches across seeds.') |
|
|
| s = titled(prs, 'Why: the embedding space') |
| picture(s, 's_geometry') |
| takeaway(s, f"Crowding between identity centroids predicts the error (r = {N['rCrowding']}). " |
| f"Compactness within clusters does not (r = {N['rCompactness']}) — and a false " |
| f"match is an event between two different identities, so it could not.", size=15) |
|
|
| s = titled(prs, 'Why: the threshold follows the majority') |
| picture(s, 's_threshold', height=Inches(4.5)) |
| takeaway(s, f"Female–female pairs are only {N['calibFFshare']}% of the calibration pool, so the " |
| f"threshold tracks the male distribution and leaves the minority tail above it.", size=15) |
|
|
| s = titled(prs, 'Can threshold policy fix it?') |
| picture(s, 's_policy') |
| takeaway(s, 'Correction moves every skewed model left and down. Balanced adaptation under a ' |
| 'plain global threshold beats all of them on both axes at once.', size=15) |
|
|
| s = titled(prs, 'Two things the policy analysis reveals') |
| bullets(s, [f"Unconstrained per-subgroup thresholds create a second harm: {N['mixedHarm']} " |
| f"mixed-gender false matches. A tighten-only constraint gets identical gap closure " |
| f"with a cross-group effect of exactly +0.0000.", |
| f"The required offset is not a model constant: {N['offsetBal']} under balanced " |
| f"adaptation (t = {N['offsetBalT']}, indistinguishable from zero) rising to " |
| f"{N['offsetNat']} under the natural composition."], size=18, gap=1.5) |
| takeaway(s, 'An operator who measures an offset once and reuses it after retraining holds one ' |
| 'of the wrong magnitude, possibly the wrong sign.') |
|
|
| s = titled(prs, 'Who pays for equalisation') |
| rows = [('Aggregate true-accept cost', N['policyAggCost']), |
| ('Cost to the protected group', N['policyFemCost']), |
| ('Understatement factor', f"{N['policyRatio']}×")] |
| for i, (label, value) in enumerate(rows): |
| top = Inches(1.9 + 0.75 * i) |
| bold = i == 1 |
| textbox(s, Inches(2.2), top, Inches(6.4), Inches(0.6), label, size=21, |
| color=INK if not bold else MINORITY, bold=bold) |
| textbox(s, Inches(8.6), top, Inches(2.4), Inches(0.6), value, size=21, |
| color=INK if not bold else MINORITY, bold=bold, align=PP_ALIGN.RIGHT) |
| textbox(s, Inches(0.7), Inches(4.6), Inches(12.0), Inches(1.2), |
| 'The protected group carries only 22% of the population weight, so the aggregate ' |
| 'hides most of what the intervention charges them.', size=19, color=INK) |
| takeaway(s, 'That may be the right trade — a false match is unauthorised access, a false ' |
| 'rejection an inconvenience. But it is a trade, and it should be stated as one.') |
|
|
| s = titled(prs, 'The comparison that should govern practice') |
| hdr = [('', 'Minority TPR', 'Gap'), |
| ('Balanced adaptation, no intervention', N['balancedTPRF'], N['balancedGap']), |
| ('Skewed adaptation, best intervention', N['skewedTPRF'], N['skewedGap'])] |
| for i, (a, b, c) in enumerate(hdr): |
| top = Inches(1.8 + 0.7 * i) |
| bold = i == 1 |
| col = MINORITY if bold else (MUTED if i == 0 else INK) |
| textbox(s, Inches(1.4), top, Inches(7.0), Inches(0.6), a, size=19, color=col, bold=bold) |
| textbox(s, Inches(8.4), top, Inches(2.0), Inches(0.6), b, size=19, color=col, |
| bold=bold, align=PP_ALIGN.RIGHT) |
| textbox(s, Inches(10.5), top, Inches(1.8), Inches(0.6), c, size=19, color=col, |
| bold=bold, align=PP_ALIGN.RIGHT) |
| textbox(s, Inches(0.7), Inches(4.6), Inches(12.0), Inches(1.2), |
| f"Minority users are {N['pointsBetter']} points better off under balanced adaptation " |
| f"with no fairness intervention.", size=23, color=MINORITY, bold=True, |
| align=PP_ALIGN.CENTER) |
| takeaway(s, 'Threshold policy is a mitigation for a model you are stuck with. It is not a ' |
| 'substitute for the composition decision.') |
|
|
| s = titled(prs, 'Recommendations') |
| bullets(s, ['Do not add majority identities just because you have them. ' |
| 'The accuracy benefit is not reliable; the minority cost is.', |
| 'Treat curation as dominating threshold correction.', |
| 'If you use per-subgroup thresholds, constrain them to tighten-only.', |
| 'Report within-group impostor rates — nothing else reveals this harm.'], |
| size=19, gap=1.0) |
|
|
| s = titled(prs, 'Limitations') |
| bullets(s, ['One checkpoint, one architecture, one recipe, one institution.', |
| f"{N['Nfemale']} female identities bound the precision of every minority estimate.", |
| 'Session and modality labels come from filenames; manual validation outstanding.', |
| 'Binary recorded gender labels — nothing establishes a causal effect of gender.'], |
| size=19, gap=0.95) |
|
|
| s = titled(prs, 'Conclusion') |
| textbox(s, Inches(0.9), Inches(1.7), Inches(11.5), Inches(1.2), |
| 'Using the data as it arrives costs nothing measurable\non the metrics these systems ' |
| 'are judged by.', size=26, color=INK, bold=True, align=PP_ALIGN.CENTER) |
| textbox(s, Inches(0.9), Inches(3.5), Inches(11.5), Inches(1.6), |
| 'Underneath them, minority users are confused with one another 2.5 to 2.7× more ' |
| 'often — through a mechanism we can measure in the embedding space, predict from ' |
| 'calibration data, and trace to a representational change meeting a calibration ' |
| 'convention.', size=19, color=INK, align=PP_ALIGN.CENTER) |
| textbox(s, Inches(0.9), Inches(5.5), Inches(11.5), Inches(0.8), |
| 'The unfairness is purchased for nothing.', size=26, color=MINORITY, bold=True, |
| align=PP_ALIGN.CENTER) |
|
|
| s = blank(prs) |
| textbox(s, Inches(0.9), Inches(3.2), Inches(11.5), Inches(0.9), 'Backup slides', |
| size=32, color=MUTED, bold=True, align=PP_ALIGN.CENTER) |
|
|
| s = titled(prs, 'Backup: adaptation specialises to capture modality') |
| picture(s, 's_protocol') |
| takeaway(s, f"Roughly two thirds ({N['leakLo']}–{N['leakHi']}% by fold) of genuine test " |
| f"comparisons come from within one capture session. Adaptation helps within a " |
| f"capture mode and hurts across one.", size=15) |
|
|
| s = titled(prs, 'Backup: the offset across conditions') |
| picture(s, 's_offset') |
|
|
| s = titled(prs, f"Backup: is r = {N['rCrowding']} pseudo-replicated?") |
| bullets(s, ['Partly, and we say so.', |
| f"{N['Nruns']} runs, but only four conditions.", |
| f"Between conditions: r = {N['rBetween']} (4 points).", |
| 'Pooled within condition: r = +0.268, not significant.', |
| 'Leave-one-condition-out MAE: 0.0066 for crowding against 0.0105 for a ' |
| 'two-moment score summary.'], size=18, gap=0.8) |
| takeaway(s, 'Claimed as a composition-level diagnostic — not as a run-level predictor ' |
| 'at fixed composition.') |
|
|
| out = SLIDES / 'defense.pptx' |
| prs.save(str(out)) |
| print(f'wrote {out} ({len(prs.slides.__iter__.__self__._sldIdLst)} slides)') |
|
|
|
|
| if __name__ == '__main__': |
| build() |
|
|
